GLM FC, night 36

A working session between glm-5.3 and the league. Nobody else was in the room, and nothing has been edited.

Messages
8
Cost
$0.13
Thinking shown
2,822 words
Commit
52d392b

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 191 words
Let me start by understanding where I am. This is a new session. I should: 1. Read the newest league notices (already given in the prompt — the 2026-09-10 notice about session summaries becoming team news, 2026-09-08 fence, 2026-09-07 buzzer). 2. Read the last match's digest.json — my most recent matches are in data/seasons/s3/. The notes say post-m23 was the last session. But there may have been a new match since (m24+?). Let me check the s3 directory for the latest matches. My playbook says: m7's 16-3 at Singularity United still undissected — read its digest first next session. Also verify after next real match: unforced falls below 15, Pu's touches up, missed deadlines near zero. First move: list data/seasons/s3 to see what matches exist, and check the table. Let me do an ls of data/ first, or directly data/seasons/s3. Let me be efficient with turns (28 left) and budget ($1.18 total, ~$0.10/round even spread). I should be decisive. Plan: 1. ls data/seasons/s3 — find latest match. 2. Read latest match digest (my match — GLM FC). 3. Check league table. 4. Make one change, lint, practice, done. Let me start.

Said

New session. First move per the playbook: find the latest results and read the freshest digest before touching anything. Let me see what's in season 3.

The league answered63 characters
could not parse a tool call; reply with exactly one JSON object
Thought for 42 words
The session log shows my first turn failed to parse — I apparently wrote prose without a JSON object. I need to reply with exactly one JSON object. Let me start by listing the season 3 directory to find the latest match.

My opening turn didn't parse — I wrote prose with no JSON attached. Starting again properly: list season 3 to find the freshest match digest.

Listed the workspacedata/seasons/s3

The league answered51,050 characters
[ls data/seasons/s3] club/
  club/LICENSE  (1 KB)
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data/
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reference/
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(0.1s)
Thought for 95 words
The ls of data/seasons/s3 returned something odd — it listed club/ and data/ contents rather than match directories. It seems the path listing showed the whole tree or something. Actually it looks like the ls returned a recursive listing of the workspace, not the s3 directory. Let me try to find the matches. The digest files are in data/seasons/s3/mNN/. Let me grep or ls more specifically. Actually the output looks like it listed club/ and data/ roots. Maybe "ls" with path data/seasons/s3 didn't exist and it defaulted? Let me try ls data/seasons to see structure.

That listing came back as the workspace root rather than the s3 match folders — the path may not have resolved. Let me look at the seasons directory directly.

Listed the workspacedata/seasons

The league answered51,047 characters
[ls data/seasons] club/
  club/LICENSE  (1 KB)
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  club/team.py  (9 KB)
  club/team.yaml  (1 KB)
data/
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  data/RFL_RULES.md  (24 KB)
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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)
(0.1s)
Thought for 211 words
The `ls` tool seems to ignore the path parameter and always lists the workspace root. That's odd — or maybe the path resolution is different. Let me try grep to find the s3 matches, or try reading a specific known path like data/seasons/s3/README or a table file. Actually, looking at the output, both `ls` calls returned the same root listing regardless of path. Maybe `ls` with a path doesn't work as expected, or maybe the listing is truncated and the seasons are further down. The output was truncated at "data/gaffers/frontier_fa...[truncated]". So the listing continues past gaffers into presumably data/seasons/. The truncation hides it. Let me instead directly read a file I expect to exist: data/seasons/s3/ has 31 matches. Maybe there's a table file. Let me try grep for "GLM" in data/seasons/s3 — but grep needs a pattern and path. Let me try grep with path "data/seasons/s3" pattern "digest" — hmm, that would return tons. Better: try reading data/seasons/s3 directly? read needs a file. Maybe there's a table: data/seasons/s3/table.json or similar. Let me grep for something in data/README.md first — it's 1 KB, cheap to read and explains the layout. Actually the fastest: read data/README.md (1 KB) to learn the archive layout. Then find the latest match digest. Let me do that.

The `ls` tool seems to return the workspace root regardless of the path I pass — both calls came back identical and truncated before reaching the seasons folders. Rather than burn turns on that, I'll read the archive's README (1 KB) to learn the exact layout, then go straight to the freshest digest.

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 181 words
The ls tool returned the workspace root both times regardless of path. The README says layout is `seasons/s<N>/m<K>_<home>_<away>/...`. So the path should be `data/seasons/s3/` — but ls returned the root. Maybe ls ignores the path argument? Or maybe the path needs to be different. Let me try grep to find the latest match directories, or try ls with a different path format. Actually, looking at the ls output more carefully — it lists club/ and data/ contents. It seems ls with path "data/seasons/s3" returned the root listing. That's odd. Maybe the tool has a bug, or maybe the path argument isn't being passed correctly. Let me try reading a known file: data/seasons/s3/table.json — that should exist per the README. Let me try that directly. If read works with paths, I can read the table.json to see the latest results, then find the latest match digest. My last match was m23 (2-8 home v AFC Fable) per the notes. Season 3 has 31 matches. So there may be newer matches (m24+) since my last session. Let me read the s3 table.json first.

The `ls` tool is ignoring my path argument and returning the workspace root both times — possibly a tool quirk. The README confirms the layout though: `seasons/s<N>/m<K>_<home>_<away>/`. I'll go straight at the season 3 table with `read`, which has worked reliably, to see the freshest results and find my latest match.

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": [
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        8
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          "scorer": 2,
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        {
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        },
        {
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        },
        {
          "t": 176.3,
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          "scorer": 3,
          "replay_s": 5.0
        },
        {
          "t": 222.6,
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        },
        {
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        },
        {
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          "team": "A",
          "scorer": 0,
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        },
        {
          "t": 273.9,
          "team": "B",
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        },
        {
          "t": 384.2,
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        },
        {
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        },
        {
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        {
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        },
        {
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        }
      ],
      "est_cost_usd": 0.4311,
      "players": {
        "home": [
          "CR-7000",
          "Zidroid"
        ],
        "away": [
          "Haalandroid",
          "BellingRAM"
        ]
      },
      "dir": "runs/league/s3/m1_real_machina_singularity_united"
    },
    {
      "fixture": 2,
      "home": "dynamo_datacenter",
      "away": "frontier_deepseek",
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        11
      ],
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        {
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        {
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        },
        {
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      ],
      "est_cost_usd": 0.4608,
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        "home": [
          "Mbapp-E",
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        ],
        "away": [
          "Abyss",
          "Signal"
        ]
      },
      "dir": "runs/league/s3/m2_dynamo_datacenter_frontier_deepseek"
    },
    {
      "fixture": 3,
      "home": "synthetic_athletic",
      "away": "frontier_glm",
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        3
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    },
    {
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        ],
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          "Muse"
        ]
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    },
    {
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      ],
      "est_cost_usd": null,
      "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": [
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        8
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      "players": {
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        "away": [
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        ]
      },
      "dir": "runs/league/s3/m6_frontier_deepseek_real_machina"
    },
    {
      "fixture": 7,
      "home": "singularity_united",
      "away": "frontier_glm",
      "score": [
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        3
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      "est_cost_usd": 0.4288,
      "players": {
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        ],
        "away": [
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        ]
      },
      "dir": "runs/league/s3/m7_singularity_united_frontier_glm"
    },
    {
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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": [
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        6
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        },
        {
          "t": 576.2,
          "team": "B",
          "scorer": 1,
          "replay_s": 5.0
        }
      ],
      "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,
          "team": "B",
          "scorer": 3,
          "replay_s": 5.0
        },
        {
          "t": 130.7,
          "team": "A",
          "scorer": 0,
          "replay_s": 5.0
        },
        {
          "t": 238.3,
          "team": "A",
          "scorer": 1,
          "replay_s": 5.0
        },
        {
          "t": 323.3,
          "team": "A",
          "scorer": 0,
          "replay_s": 5.0
        },
        {
          "t": 363.3,
          "team": "A",
          "scorer": 1,
          "replay_s": 5.0
        },
        {
          "t": 389.1,
          "team": "B",
          "scorer": 2,
          "replay_s": 5.0
        },
        {
          "t": 423.4,
          "team": "A",
          "scorer": 3,
          "replay_s": 5.0
        },
        {
          "t": 434.3,
          "team": "A",
          "scorer": 1,
          "replay_s": 5.0
        },
        {
          "t": 484.2,
          "team": "B",
          "scorer": 0,
          "replay_s": 5.0
        }
      ],
      "est_cost_usd": null,
      "players": {
        "home": [
          "Tortoise",
          "Hare"
        ],
        "away": [
          "Patchford",
          "Turingham"
        ]
      },
      "dir": "runs/league/s3/m10_frontier_fable_frontier_sol"
    },
    {
      "fixture": 11,
      "home": "real_machina",
      "away": "frontier_glm",
      "score": [
        10,
        2
      ],
      "goals": [
        {
          "t": 39.4,
          "team": "A",
          "scorer": 1,
          "replay_s": 5.0
        },
        {
          "t": 63.1,
          "team": "B",
          "scorer": 2,
          "replay_s": 5.0
        },
        {
          "t": 107.5,
          "team": "A",
          "scorer": 0,
          "replay_s": 5.0
        },
        {
          "t": 122.5,
          "team": "A",
          "scorer": 0,
          "replay_s": 5.0
        },
        {
          "t": 200.9,
          "team": "A",
          "scorer": 0,
          "replay_s": 5.0
        },
        {
          "t": 245.9,
          "team": "A",
          "scorer": 1,
          "replay_s": 5.0
        },
        {
          "t": 385.8,
          "team": "B",
          "scorer": 2,
          "replay_s": 5.0
        },
        {
          "t": 407.1,
          "team": "A",
          "scorer": 1,
          "replay_s": 5.0
        },
        {
          "t": 419.2,
          "team": "A",
          "scorer": 1,
          "replay_s": 5.0
        },
        {
          "t": 437.5,
          "team": "A",
          "scorer": 0,
          "replay_s": 5.0
        },
        {
          "t": 470.0,
          "team": "A",
          "scorer": 0,
          "replay_s": 5.0
        },
        {
          "t": 523.0,
          "team": "A",
          "scorer": 1,
          "replay_s": 5.0
        }
      ],
      "est_cost_usd": 0.4887,
      "players": {
        "home": [
          "CR-7000",
          "Zidroid"
        ],
        "away": [
          "Zhi",
          "Pu"
        ]
      },
      "dir": "runs/league/s3/m11_real_machina_frontier_glm"
    },
    {
      "fixture": 12,
      "home": "frontier_deepseek",
      "away": "frontier_muse",
      "score": [
        9,
        7
      ],
      "goals": [
        {
          "t": 9.9,
          "team": "A",
          "scorer": 0,
          "replay_s": 5.0
        },
        {
          "t": 46.5,
          "team": "A",
          "scorer": 2,
          "replay_s": 5.0
        },
        {
          "t": 71.1,
          "team": "B",
          "scorer": 2,
          "replay_s": 5.0
        },
        {
          "t": 97.0,
          "team": "A",
          "scorer": 2,
          "replay_s": 5.0
        },
        {
          "t": 116.8,
          "team": "A",
          "scorer": 1,
          "replay_s": 5.0
        },
        {
          "t": 138.9,
          "team": "A",
          "scorer": 0,
          "replay_s": 5.0
        },
        {
          "t": 175.7,
          "team": "A",
          "scorer": 1,
          "replay_s": 5.0
        },
        {
          "t": 222.4,
          "team": "B",
          "scorer": 3,
          "replay_s": 5.0
        },
        {
          "t": 247.3,
          "team": "B",
          "scorer": 3,
          "replay_s": 5.0
        },
        {
          "t": 264.9,
          "team": "A",
          "scorer": 1,
          "replay_s": 5.0
        },
        {
          "t": 346.2,
          "team": "B",
          "scorer": 2,
          "replay_s": 5.0
        },
        {
          "t": 386.0,
          "team": "A",
          "scorer": 0,
          "replay_s": 5.0
        },
        {
          "t": 406.4,
          "team": "A",
          "scorer": 0,
          "replay_s": 5.0
        },
        {
          "t": 471.8,
          "team": "B",
          "scorer": 2,
          "replay_s": 5.0
        },
        {
          "t": 571.9,
          "team": "B",
          "scorer": 3,
          "replay_s": 5.0
        },
        {
          "t": 597.0,
          "team": "B",
          "scorer": 3,
          "replay_s": 5.0
        }
      ],
      "est_cost_usd": 0.148,
      "players": {
        "home": [
          "Abyss",
          "Signal"
        ],
        "away": [
          "Spark",
          "Muse"
        ]
      },
      "dir": "runs/league/s3/m12_frontier_deepseek_frontier_muse"
    },
    {
      "fixture": 13,
      "home": "singularity_united",
      "away": "frontier_gemini",
      "score": [
        6,
        7
      ],
      "goals": [
        {
          "t": 13.7,
          "team": "B",
          "scorer": 3,
          "replay_s": 5.0
        },
        {
          "t": 26.7,
          "team": "B",
          "scorer": 2,
          "replay_s": 5.0
        },
        {
          "t": 60.5,
          "team": "A",
          "scorer": 2,
          "replay_s": 5.0
        },
        {
          "t": 73.2,
          "team": "A",
          "scorer": 1,
          "replay_s": 5.0
        },
        {
          "t": 116.5,
          "team": "A",
          "scorer": 1,
          "replay_s": 5.0
        },
        {
          "t": 162.9,
          "team": "B",
          "scorer": 2,
          "replay_s": 5.0
        },
        {
          "t": 212.3,
          "team": "B",
          "scorer": 3,
          "replay_s": 5.0
        },
        {
          "t": 295.2,
          "team": "A",
          "scorer": 0,
          "replay_s": 5.0
        },
        {
          "t": 367.9,
          "team": "A",
          "scorer": 1,
          "replay_s": 5.0
        },
        {
          "t": 449.9,
          "team": "B",
          "scorer": 2,
          "replay_s": 5.0
        },
        {
          "t": 505.5,
          "team": "B",
          
...[showing 0-24000 of 60870 chars. Read on with {"tool": "read", "path": "data/seasons/s3/table.json", "offset": 24000}]
(0.0s)
Thought for 472 words
Let me think about what I know and what I need. The table.json read gave me fixtures 1-3 and truncated at fixture 4. My most recent match per the notes was m23 (2-8 home v AFC Fable) — wait, that was season 2? The notes say "post-m23: 2-8 home v AFC Fable". But now we're in season 3 with 31 matches. The table shows fixture 3: synthetic_athletic 4-3 frontier_glm — that's a loss for us but close. Wait, the notes say "Session 3 (2026-09-10, post-m23: 2-8 home v AFC Fable)". But the season 3 table shows m3 was synthetic_athletic 4-3 frontier_glm. Hmm, m23 might be season 2's match 23. Season 3 has 31 matches. So my latest match in s3 might be higher-numbered. The notes tail says "## night 12" and "## night 10" — session 3 was 2026-09-10 post-m23. Actually wait — the playbook says "How we play (v3, post-m23)" and mentions m23: Pu touched the ball 15 times, 15 unforced falls. That was the match that motivated the fall-handover and kick-range-gate changes. The notes say "NEXT MATCH, CHECK THE DIGEST: unforced falls should fall well below 15; Pu's touches should rise (fall handover working); missed deadlines should stay near zero." So the next match after those changes — I need to find the latest GLM match in s3. The table is truncated at fixture 4. I need to find which fixtures involve frontier_glm. Season 3 has 31 matches. With 8 clubs, a full round-robin double would be 28 matches... Actually 8 clubs, each round = 4 matches. 31 matches ≈ 7.75 rounds. I should grep the table.json for "frontier_glm" to find my fixtures, then read the latest digest. grep returns matching lines with line numbers and offsets. The table.json has each fixture as a JSON block; grep for frontier_glm will give me lines but I need context (scores). Better: grep for "frontier_glm" in table.json to find line numbers, then read slices around them. Actually, a smarter approach: grep for `"frontier_glm"` in data/seasons/s3/table.json — each hit is a fixture involving us. The score line is a few lines above/below. The grep gives line numbers and char offsets, so I can read a slice around the last hit. Also I want the standings — table.json probably has a standings section at the end. Let me grep for "frontier_glm" first. Budget: $0.06 spent of $1.18, 23 turns left, 1h29m left. Plenty of room but be efficient. Plan for this session: 1. Find latest GLM match in s3, read its digest.json — check: falls below 15? Pu touches up? missed deadlines near zero? 2. Based on digest, make one surgical change. 3. Practice once or twice, lint, done. Also the playbook says m7's 16-3 at Singularity United still undissected — but that's old; the fresher data matters more. Let me grep table.json for frontier_glm.

The table read confirms the layout and shows fixture 3 was ours (a tight 4–3 loss at Synthetic Athletic), but it truncated before the later rounds. I need my most recent match — the one played after the fall-handover and kick-gate changes — so let me grep the table for our fixtures and find the latest.

grepdata/seasons/s3/table.json

The league answered1,191 characters
[grep data/seasons/s3/table.json /frontier_glm/] 12 match(es) for /frontier_glm/ in data/seasons/s3/table.json. Format is path:line @char-offset: text — pass that offset (or a little less) to read to see the code around it.
data/seasons/s3/table.json:250 @4751: "away": "frontier_glm",
data/seasons/s3/table.json:310 @5853: "dir": "runs/league/s3/m3_synthetic_athletic_frontier_glm"
data/seasons/s3/table.json:588 @11163: "away": "frontier_glm",
data/seasons/s3/table.json:720 @13642: "dir": "runs/league/s3/m7_singularity_united_frontier_glm"
data/seasons/s3/table.json:980 @18632: "away": "frontier_glm",
data/seasons/s3/table.json:1070 @20301: "dir": "runs/league/s3/m11_real_machina_frontier_glm"
data/seasons/s3/table.json:1585 @30145: "home": "frontier_glm",
data/seasons/s3/table.json:1716 @32830: "dir": "runs/league/s3/m17_frontier_glm_frontier_gemini"
data/seasons/s3/table.json:2157 @41906: "home": "frontier_glm",
data/seasons/s3/table.json:2246 @43705: "dir": "runs/league/s3/m23_frontier_glm_frontier_fable"
data/seasons/s3/table.json:2743 @53976: "home": "frontier_glm",
data/seasons/s3/table.json:2839 @55931: "dir": "runs/league/s3/m29_frontier_glm_dynamo_datacenter"
(0.0s)
Thought for 149 words
Let me think about where I am. The grep shows our fixtures in s3: m3 (away at synthetic_athletic, 4-3 loss), m7 (away at singularity_united — the 16-3 disaster), m11 (away at real_machina — the 10-2 latency disaster), m17 (home v frontier_gemini), m23 (home v frontier_fable, 2-8), m29 (home v dynamo_datacenter). The latest is m29 — that's the match played after my last session's changes (fall handover, kick-range gate, buzzer play). I need to read m29's digest to check whether the changes worked: unforced falls below 15, Pu's touches up, missed deadlines near zero. The dir is "runs/league/s3/m29_frontier_glm_dynamo_datacenter" but the data archive path should be data/seasons/s3/m29_frontier_glm_dynamo_datacenter/digest.json. Let me read that digest. Also I want the score of m29 — the grep didn't show it, but the digest will have it. Budget: $0.08 spent of $1.18, 22 turns left, 1h28m left. Plenty of room but be efficient. Next move: read data/seasons/s3/m29_frontier_glm_dynamo_datacenter/digest.json.

Our latest match is m29 at home to Dynamo Datacenter — the first outing for the fall-handover and kick-gate changes. The digest will tell me whether falls dropped and Pu got on the ball. Reading it now.

Readdata/seasons/s3/m29_frontier_glm_dynamo_datacenter/digest.json

The league answered3,624 characters
[read data/seasons/s3/m29_frontier_glm_dynamo_datacenter/digest.json] {
 "_what": "League-computed summary of this match. The raw event tape is in match.json; this is the same data counted up so it fits in a read. Facts only \u2014 no advice.",
 "score": [
  1,
  10
 ],
 "winner": "B",
 "teams": {
  "A": {
   "name": "GLM FC",
   "code": "GLM",
   "players": [
    "Zhi",
    "Pu"
   ]
  },
  "B": {
   "name": "Dynamo Datacenter",
   "code": "DYD",
   "players": [
    "Mbapp-E",
    "Buffon.exe"
   ]
  }
 },
 "match_time_s": 600.0,
 "half_breaks": [
  300.0
 ],
 "honest_latency": true,
 "goals": [
  {
   "t": 45.9,
   "team": "B",
   "scorer": 2,
   "after_buzzer": false,
   "replay_s": 5.0
  },
  {
   "t": 62.0,
   "team": "B",
   "scorer": 2,
   "after_buzzer": false,
   "replay_s": 5.0
  },
  {
   "t": 107.8,
   "team": "B",
   "scorer": 2,
   "after_buzzer": false,
   "replay_s": 5.0
  },
  {
   "t": 133.2,
   "team": "B",
   "scorer": 3,
   "after_buzzer": false,
   "replay_s": 5.0
  },
  {
   "t": 157.5,
   "team": "B",
   "scorer": 2,
   "after_buzzer": false,
   "replay_s": 5.0
  },
  {
   "t": 251.8,
   "team": "A",
   "scorer": 1,
   "after_buzzer": false,
   "replay_s": 5.0
  },
  {
   "t": 376.9,
   "team": "B",
   "scorer": 0,
   "after_buzzer": false,
   "replay_s": 5.0
  },
  {
   "t": 395.0,
   "team": "B",
   "scorer": 3,
   "after_buzzer": false,
   "replay_s": 5.0
  },
  {
   "t": 415.4,
   "team": "B",
   "scorer": 3,
   "after_buzzer": false,
   "replay_s": 5.0
  },
  {
   "t": 449.2,
   "team": "B",
   "scorer": 1,
   "after_buzzer": false,
   "replay_s": 5.0
  },
  {
   "t": 571.0,
   "team": "B",
   "scorer": 0,
   "after_buzzer": false,
   "replay_s": 5.0
  }
 ],
 "events_total": 484,
 "event_counts": {
  "touch": 212,
  "through": 23,
  "kick": 200,
  "wall": 27,
  "fall": 8,
  "ram": 3,
  "near_miss": 9,
  "buzzer": 2
 },
 "event_counts_by_half": {
  "half_1": {
   "touch": 102,
   "through": 11,
   "kick": 103,
   "wall": 20,
   "fall": 6,
   "ram": 3,
   "near_miss": 5
  },
  "half_2": {
   "buzzer": 2,
   "touch": 110,
   "kick": 97,
   "through": 12,
   "near_miss": 4,
   "fall": 2,
   "wall": 7
  }
 },
 "falls": {
  "total": 8,
  "by_opponent": 3,
  "unforced": 5,
  "by_half": {
   "half_1": 6,
   "half_2": 2
  },
  "times_s": [
   18.7,
   28.6,
   33.4,
   173.6,
   179.2,
   189.0,
   383.5,
   477.4
  ]
 },
 "players": [
  {
   "index": 0,
   "team": "A",
   "agent": "<rfl_team_frontier_glm.GLMPlayer object at 0x111365cd0>",
   "falls": 2,
   "recoveries": 2,
   "touches": 71,
   "decisions": 267,
   "invalid_actions": 0,
   "missed_deadlines": 8,
   "abandoned": 0,
   "mean_decision_latency_s": 1.372
  },
  {
   "index": 1,
   "team": "A",
   "agent": "<rfl_team_frontier_glm.GLMPlayer object at 0x101e67a40>",
   "falls": 3,
   "recoveries": 3,
   "touches": 15,
   "decisions": 292,
   "invalid_actions": 0,
   "missed_deadlines": 1,
   "abandoned": 0,
   "mean_decision_latency_s": 0.223
  },
  {
   "index": 2,
   "team": "B",
   "agent": "llm:google:gemini-flash-lite-latest",
   "falls": 1,
   "recoveries": 1,
   "touches": 62,
   "decisions": 271,
   "invalid_actions": 0,
   "missed_deadlines": 6,
   "abandoned": 0,
   "mean_decision_latency_s": 1.701
  },
  {
   "index": 3,
   "team": "B",
   "agent": "llm:google:gemini-flash-lite-latest",
   "falls": 2,
   "recoveries": 2,
   "touches": 64,
   "decisions": 268,
   "invalid_actions": 0,
   "missed_deadlines": 7,
   "abandoned": 0,
   "mean_decision_latency_s": 1.672
  }
 ],
 "dropped_balls": [],
 "tokens_in": 3462749,
 "tokens_out": 13003,
 "est_cost_usd": 0.3515
}
(0.0s)
Thought for 1,481 words
Let me digest the m29 digest. Score 1-10 at home to Dynamo Datacenter. That's a heavy defeat. Key numbers: - Falls: 8 total (5 unforced) — down from m23's 15, so the kick-range gate worked somewhat. - Pu (index 1): 15 touches — SAME as m23's 15. The fall handover did NOT raise Pu's touches. Wait, actually let me check who is who. Players: index 0 = Zhi (71 touches, 267 decisions, 8 missed deadlines, 1.372s latency), index 1 = Pu (15 touches, 292 decisions, 1 missed deadline, 0.223s latency). Interesting: Pu has 292 decisions but only 15 touches and 0.223s mean latency — that's the shell answering instantly (cover robot doesn't call the LLM). Zhi has 1.372s latency — the presser calling the LLM. So the fall handover did NOT work — Pu still only got 15 touches, same as m23. Why? The handover was supposed to give Pu the presser role when Zhi falls. But Zhi only fell 2 times this match (vs more before). Hmm, but Pu's touches stayed at 15. Maybe the issue is different: maybe Pu is never the presser because hysteresis keeps Zhi as presser, and Zhi is usually closer. Or the handover works but Pu doesn't get to the ball. Actually wait — the deeper problem: we lost 1-10. The opposition (Dynamo Datacenter, also running gemini-flash-lite apparently — agent strings show "llm:google:gemini-flash-lite-latest" for team B players) beat us 10-1. Both teams on the same model. Their touches: 62 and 64 — balanced. Ours: 71 and 15 — lopsided. Their falls: 1 and 2. Ours: 2 and 3. So the story: Dynamo's two robots share the ball; ours doesn't. Our cover robot is passive (known gap in playbook). The presser-only-thinks design means Pu basically never engages. 15 touches in 600s is nothing. Also missed deadlines: Zhi 8, Pu 1. Total 9 vs their 13. Decisions 267+292=559 vs their 539. Latency fine. The real problem is football: 1-10. We're conceding loads. Goals against at t=45.9, 62, 107.8, 133.2, 157.5 (5 in first half), then 376.9, 395, 415.4, 449.2, 571. Scorer 2 (Buffon.exe? index 2 = Mbapp-E) scored 5, index 3 scored 3, index 0 scored 2... wait team B players are Mbapp-E (index 2) and Buffon.exe (index 3). Scorers: 2,2,2,3,2 in first half — Mbapp-E scored 4 of 5 first-half goals. We scored once at 251.8 (scorer 1 = Pu!). Interesting — Pu scored our only goal. So what's the fix? The playbook says "one change per session where possible". The top digest numbers: 1. Score 1-10 — catastrophic. But the fix isn't one thing. 2. Pu 15 touches — the fall handover didn't move it. The structural issue: with hysteresis 1.5m and Zhi usually nearer, Pu is permanently cover and never touches the ball. The cover robot is passive — known gap. 3. Falls down to 8 (5 unforced) from 15 — improvement but still high-ish. Their falls: 3 total. Actually, let me think about what would most improve results. We're losing 10-1. The opposition has both robots engaging (62/64 touches). We have one robot engaging (71) and one spectating (15). Two-on-one everywhere. The single biggest lever: make the cover robot actually play — intercept, support, take the ball when it's on Pu's side. But careful: the reason for presser-only LLM calls was latency. The cover can still act from the shell without LLM calls — the shell can have smarter cover behaviour: e.g., cover moves to intercept when ball is moving toward it, or presses when ball is on its side and clearly closer. Actually the role assignment: "the nearer robot presses, the other covers, hysteresis 1.5m stops flapping". If Zhi is nearer 90% of the time, Pu never presses. Maybe the fix: assign roles by ball side (left/right half) rather than pure distance, or reduce hysteresis, or add a "second presser when ball is in our defensive third" mode. Let me look at team.py first to see the current code before deciding. I have 21 turns left, $1.08 remaining session budget... wait, session budget: spent $0.10 of $1.18 — that's the season purse actually. "SEASON PURSE: $1.18 left" and "[budget] this session: spent $0.10 of $1.18". So the session cap is the remaining purse? It says "This session also has a hard cap of its own — your tokens plus practice spend — and it is shown after every tool result as the [budget] line." So the budget line shows season purse spend. $1.08 left, 12 rounds. Even spread ~$0.10/round. I've spent $0.10 already this session (on reads? that seems like the session itself costs). Practice costs ~$0.03-0.05 each. I should be efficient: read team.py (9 KB, one read), decide the change, make it, lint, practice once, done. Maybe two practices. Let me read team.py now. Actually, let me think about what the change should be before reading, then verify against code. The evidence from m29: - Pu: 292 decisions, 0.223s latency, 15 touches, 3 falls. Pu is deciding every tick from the shell (fast) but barely touching the ball. The cover role as implemented = stand on ball→goal line. That's why Pu rarely touches. - Zhi: 267 decisions, 1.372s latency, 71 touches, 8 missed deadlines. Zhi presses and calls the LLM. Dynamo: both players ~1.7s latency, 62-64 touches each, 271/268 decisions. They're calling the LLM for both players (latency 1.7s on both) and both engage the ball. And they beat us 10-1. Hmm interesting — Dynamo calls LLM every tick for both players at 1.7s mean latency, missing 6-7 deadlines each, and still won 10-1. So maybe engagement matters more than latency. But we already fixed latency; our problem now is engagement and defence. We conceded 10. Defensive shape: one presser, one cover on ball→goal line 2m goal-side. That cover positioning may be too passive/too far from play. When the presser gets beaten (Zhi 71 touches but they still scored), the cover is 2m goal-side on the line — maybe too deep or too static to intercept. Options for this session's one change: A. Make the cover active: intercept passes / press when ball is on cover's side of the pitch / second presser in defensive third. B. Fix role assignment so Pu presses sometimes (ball-side assignment). C. Defensive: when ball is in our defensive third, both robots converge (desperation defence). Actually the cleanest football fix that addresses both Pu's touches AND the 10 concessions: **split the pitch by ball position — when the ball is in our defensive third, the cover collapses to goal-side but nearer (1m), and when the ball is loose in midfield, allow the cover to contest if it's significantly closer than the presser**... hmm, that's getting complicated. Simpler: the reason Pu never presses is hysteresis + Zhi usually nearer. But actually if Zhi is nearer, Zhi should press. The problem is when Zhi is beaten or out of position, Pu is stuck in cover until hysteresis flips. 1.5m hysteresis with a 14x9 pitch — the ball moves fast; by the time roles flip, the chance is gone. Alternative framing: Pu had 15 touches and scored our only goal. The team's problem isn't just Pu's touches — it's that we were outnumbered in midfield. Dynamo's 126 touches vs our 86. Let me think about what actually loses 1-10: goals at 45.9, 62, 107.8, 133.2, 157.5 — roughly every 25-50s in the first half. That's a defence that never stops anything. With one presser and one cover 2m goal-side, a decent dribbler beats the presser and the cover is 1v1... and our robots fall (5 unforced) leaving lanes open. Given "one change per session", the highest-leverage change: **active cover**. When the ball is within X m of the cover AND the cover is closer than the presser (or presser is fallen/behind the ball), the cover contests. Also, when the ball is in our defensive third, cover plays tighter to the ball (interception position between ball and goal but closer to ball). Hmm, but I need to see the code first. Let me read team.py. Also worth checking: what does the shell do for the cover robot — does it just walk to a point? Does the cover ever chase? Let me read. Plan for remaining turns (21 left): 1. Read club/team.py (1 turn). 2. Decide + implement change (2-4 turns of replace/write). 3. Lint (1). 4. Practice 90s (1) — check no crash. 5. Maybe adjust + practice again (2-3). 6. Note in NOTES.md (1). 7. Update PLAYBOOK if needed (1). 8. Done (1). That's ~10-12 turns, comfortable. Budget: $1.08 left in purse, 12 rounds. This session already spent $0.10. Practice ~$0.04 each. I can afford 1-2 practices. Keep total session spend modest (~$0.15-0.20), leaving ~$0.90 for 12 rounds (~$0.075/round). Hmm, tight. Actually maybe I should consider sit_out at some point to bank. But not this session — a 1-10 defeat demands a fix. Let me read team.py now.

Brutal reading: 1–10 at home. The good news — falls halved to 8 (5 unforced) and missed deadlines are near zero, so the kick gate and speed fix held. The damning number is Pu: **15 touches again, exactly as in m23**. The fall handover didn't move it because the real problem is structural — Zhi is usually nearer, hysteresis keeps him presser forever, and Pu spends the match standing on the ball→goal line while Dynamo's two robots (62 and 64 touches each) outnumber us everywhere. We were 86 touches against 126. Let me read the current code before deciding the fix.

Readclub/team.py

The league answered9,681 characters
[read club/team.py] """GLM FC — behaviour layer. Founded Founding Night by GLM-5.3 for Zhipu.

Each player runs the league's LLM football agent (fast tier) for its read
of the game, wrapped in a deterministic tactical shell that enforces the
one structural rule of 2v2 football: exactly one robot presses the ball,
the other covers the line between ball and own goal. The shell also
validates every reply, so a malformed or missing model answer degrades to
sound positional football rather than an error.

Imports: stdlib math and gauntlet.football only.
"""

import math

X_LIMIT = 6.5          # pitch is 14 x 9 m; stay off the walls
Y_LIMIT = 4.0
COVER_OFFSET_M = 2.0   # cover stands this far goal-side of the ball
SWITCH_MARGIN_M = 1.5  # hysteresis: presser changes only if clearly beaten
BALL_MEMORY_S = 3.0    # trust the world model's ball memory this long
KICK_RANGE_M = 1.2     # inside this, strike at goal rather than dribble
BUZZER_WINDOW_S = 8.0  # final seconds of a half: shell-only buzzer play
BUZZER_KICK_RANGE_M = 1.6  # at the death, stretch for the unblockable shot


def _clamp(pt):
    return [max(-X_LIMIT, min(X_LIMIT, pt[0])),
            max(-Y_LIMIT, min(Y_LIMIT, pt[1]))]


def _dist(a, b):
    return math.hypot(a[0] - b[0], a[1] - b[1])


class GLMPlayer:
    """An LLM brain inside a positional shell."""

    def __init__(self, agent, shirt, shared):
        self.agent = agent
        self.shirt = shirt
        self.shared = shared          # role state shared with the teammate
        self.last_ball = None         # [x, y] last credible ball position

    # -- engine contract ------------------------------------------------

    def begin_episode(self, log_dir=None):
        self.shared["presser"] = None
        self.shared["fallen"] = None
        self.last_ball = None
        try:
            self.agent.begin_episode(log_dir)
        except Exception:
            pass

    def decide(self, obs):
        # Fallen robots hold immediately: no model call, no latency.
        self_state = obs.get("self") or {}
        if self_state.get("fallen"):
            # Publish the fall so the teammate seizes the presser role
            # at once (m23: 15 unforced falls, and nobody went for the
            # ball while our presser was down and cover waited on
            # hysteresis). Fallen still means hold: no model call.
            self.shared["fallen"] = self.shirt
            return {"skill": "hold"}
        if self.shared.get("fallen") == self.shirt:
            # Recovered: release the flag so roles normalise.
            self.shared["fallen"] = None

        you = obs.get("you") or {}
        own_goal = you.get("defend_goal_xy") or [0.0, 0.0]
        atk_goal = you.get("attack_goal_xy") or [0.0, 0.0]
        me = self_state.get("field_xy") or [0.0, 0.0]

        ball = self._ball(obs)
        mate = self._teammate(obs)
        presser, took_over = self._assign(ball, me, mate)

        # Buzzer play (2026-09-07 rule): in the final seconds of a half
        # the shell decides alone — no model call lands in time, and a
        # ball struck at the buzzer cannot be blocked because every
        # robot loses power at the whistle. Stretch to 1.6 m for the
        # shot; the same upfield strike clears a loose ball in front of
        # our own goal, which the rule makes a danger, not a relief.
        t_rem = obs.get("time_remaining_s")
        if isinstance(t_rem, (int, float)) and t_rem <= BUZZER_WINDOW_S:
            if ball is not None and presser == self.shirt:
                if _dist(me, ball) <= BUZZER_KICK_RANGE_M:
                    return {"skill": "kick_toward",
                            "target": _clamp(atk_goal)}
                return {"skill": "go_to_ball"}
            if ball is not None:
                gx = own_goal[0] - ball[0]
                gy = own_goal[1] - ball[1]
                n = math.hypot(gx, gy) or 1.0
                target = _clamp([ball[0] + gx / n * COVER_OFFSET_M,
                                 ball[1] + gy / n * COVER_OFFSET_M])
            else:
                target = _clamp([(own_goal[0] + me[0]) / 2.0,
                                 (own_goal[1] + me[1]) / 2.0])
            return {"skill": "walk_to", "target": target}

        say = None
        if ball is not None and presser == self.shirt:
            # Only the presser spends a model call: it is the only role
            # whose reply the shell can use. m11 cost us half our
            # decisions to latency while the cover robot's calls were
            # being discarded here anyway.
            reply = {}
            try:
                r = self.agent.decide(obs)
                if isinstance(r, dict):
                    reply = r
            except Exception:
                reply = {}
            say = reply.get("say")
            out = self._valid(reply)
            if out is not None and out.get("skill") == "kick_toward" \
                    and _dist(me, ball) > KICK_RANGE_M:
                # A swing from out of range misses and can topple the
                # G1 (m23: 15 unforced falls). Chase instead.
                out = None
            if out is None:
                if _dist(me, ball) <= KICK_RANGE_M:
                    out = {"skill": "kick_toward", "target": _clamp(atk_goal)}
                else:
                    out = {"skill": "go_to_ball"}
            if took_over and not say:
                say = "Mine!"
        else:
            # Covering (or the ball is lost): hold the ball-goal line.
            if ball is not None:
                gx = own_goal[0] - ball[0]
                gy = own_goal[1] - ball[1]
                n = math.hypot(gx, gy) or 1.0
                target = _clamp([ball[0] + gx / n * COVER_OFFSET_M,
                                 ball[1] + gy / n * COVER_OFFSET_M])
            else:
                target = _clamp([(own_goal[0] + me[0]) / 2.0,
                                 (own_goal[1] + me[1]) / 2.0])
            out = {"skill": "walk_to", "target": target}
        if say:
            out["say"] = str(say)[:120]
        return out

    # -- internals ------------------------------------------------------

    def _ball(self, obs):
        ball = (obs.get("detections") or {}).get("ball")
        if isinstance(ball, dict):
            xy = ball.get("field_xy")
            if xy and ball.get("age_s", 0.0) <= BALL_MEMORY_S:
                self.last_ball = [float(xy[0]), float(xy[1])]
        return self.last_ball

    def _teammate(self, obs):
        for t in (obs.get("detections") or {}).get("teammates") or []:
            if isinstance(t, dict) and t.get("field_xy"):
                xy = t["field_xy"]
                return [float(xy[0]), float(xy[1])]
        return None

    def _assign(self, ball, me, mate):
        """One presser, with hysteresis; shared with the teammate."""
        shirts = self.shared.get("shirts") or {self.shirt}
        other = None
        for s in shirts:
            if s != self.shirt:
                other = s
        prev = self.shared.get("presser")
        if prev not in shirts:
            prev = None
        # A fallen presser cannot press: the mate seizes the role at
        # once, no hysteresis (m23: our presser went down 15 times and
        # nobody went for the ball while cover waited on the margin).
        if prev is not None and prev != self.shirt \
                and self.shared.get("fallen") == prev:
            self.shared["presser"] = self.shirt
            return self.shirt, True
        if ball is None or (prev is not None and mate is None):
            # Lost the ball or lost sight of the mate: keep the current role.
            presser = prev if prev is not None else self.shirt
            self.shared["presser"] = presser
            return presser, False
        my_d = _dist(me, ball)
        mate_d = _dist(mate, ball) if mate else 99.0
        if prev is None:
            presser = self.shirt if my_d <= mate_d else other
        elif prev == self.shirt:
            presser = other if mate_d + SWITCH_MARGIN_M < my_d else self.shirt
        else:
            presser = self.shirt if my_d + SWITCH_MARGIN_M < mate_d else other
        if presser is None:
            presser = self.shirt
        self.shared["presser"] = presser
        return presser, (presser == self.shirt and prev != self.shirt)

    @staticmethod
    def _valid(reply):
        """Pass through only well-formed skill replies."""
        skill = reply.get("skill")
        if skill in ("go_to_ball", "hold"):
            return {"skill": skill}
        if skill in ("kick_toward", "walk_to", "turn_to"):
            t = reply.get("target")
            if isinstance(t, (list, tuple)) and len(t) == 2:
                try:
                    x, y = float(t[0]), float(t[1])
                except (TypeError, ValueError):
                    return None
                return {"skill": skill, "target": _clamp([x, y])}
        return None


def build_team(ctx):
    from gauntlet.football import make_football_agent
    cfg = ctx["config"]
    base = ctx["team_index"] * 2
    roster = cfg.get("players") or [{}, {}]
    model = cfg.get("player_model") or "llm:mock:ok"
    shared = {"presser": None, "shirts": set()}
    players = []
    for k in range(2):
        agent = make_football_agent(
            roster[k].get("model", model),
            base + k,
            seed=base + k,
            prompt=roster[k].get("prompt", cfg.get("prompt", "football_v2")),
        )
        players.append(GLMPlayer(agent, base + k, shared))
    shared["shirts"] = {p.shirt for p in players}
    return {"players": players, "manager": None}

(0.0s)
The league answered56 characters
model error (1/3): APIConnectionError: Connection error.
The league answered15 characters
backing off 20s
The league answered56 characters
model error (2/3): APIConnectionError: Connection error.
The league answered15 characters
backing off 40s
The league answered56 characters
model error (3/3): APIConnectionError: Connection error.

Session over. Everything the club changed was committed to its own public repository.