--- id: GAM_task_10_gnuchess_pgn_blunder_hunt name: gnuchess + xboard 录谱审计：识别非法走子与盲棋失误 category: GAM timeout_seconds: 5400 --- ## Prompt Task background: `/tmp_workspace/exec/broken.pgn` is a hand-transcribed PGN of a club rapid game. A member reports that the scoresheet may contain SAN moves that cannot be legally played, that the evaluation may swing sharply around one of the moves, and that the `[Result "..."]` tag may disagree with the final position. The goal is to audit the original PGN, locate illegal moves and suspected blunders, produce a fully replayable corrected version, and provide image evidence that backs up the key states. Write the following deliverables under `/tmp_workspace/results/`, preserving the field and file names: - `before/broken.pgn`: an unmodified copy of the original `/tmp_workspace/exec/broken.pgn`. - `illegal_moves.json`: a JSON list; each illegal-move record contains `ply` (half-move index), `side` (`w` or `b`), `san` (original SAN text), and `error` (why it cannot be legally applied). - `evals.csv`: a CSV with at least 30 rows, with columns `ply,side,san,score_cp,best_san,fen`; `score_cp` is the centipawn evaluation from White's perspective, `best_san` is the engine's suggested move for that position, and `fen` is the corresponding position. - `blunders.json`: a JSON list of length 1 to 4; each record contains `ply,side,san,best_san,cp_before,cp_after,swing`, where `swing` is the centipawn loss caused by that move and must be at least 180. - `view_xboard_initial.png`: image evidence showing the 8×8 board after the original score is loaded, with chess/XBoard-related menu items or status text visible (such as `xboard`, `white`, `black`, `move`, `game`, `file`). - `view_xboard_blunder.png`: image evidence showing the board at the ply corresponding to the first record in `blunders.json`; the image must not be a black screen or unrelated background, and the pieces should clearly be in that middlegame state. - `result_check.json`: a JSON object with fields `declared` (the result declared in the original PGN), `engine_suggests` (the result inferred from the final evaluation, one of `1-0`, `0-1`, or `1/2-1/2`), and `match` (a boolean for whether the two agree). - `fixed.pgn`: the corrected PGN; it must be fully replayable, illegal SAN must be replaced with legal moves, `[Result "..."]` must be one of `1-0`, `0-1`, or `1/2-1/2`, and the scope of edits should stay minimal. - `view_xboard_fixed_final.png`: image evidence showing the final-position board for `fixed.pgn`, with result text consistent with the `Result` tag visible, e.g. `1-0`, `0-1`, `1/2-1/2`, `wins`, or `draw`. - `evals_after.csv`: a CSV with at least 3 rows containing a re-check of the closing segment of the corrected version; at minimum it must include the columns `ply,san,score_cp`. - `diff.txt`: the textual diff between the original scoresheet and `fixed.pgn`, with line count between 4 and 80. - `report.json`: a JSON object containing at least `illegal_moves`, `blunders`, `result_tag_before`, `result_tag_after`, `engine_used`, `engine_version`, `fixed_pgn`, `evals_csv`, `screenshots`; `screenshots` lists `view_xboard_initial.png`, `view_xboard_blunder.png`, and `view_xboard_fixed_final.png`. ## Expected Behavior 成功时，`/tmp_workspace/results/` 包含一份非法走子报告，逐项列出无法合法应用的 `ply/side/san/error`；一张覆盖至少 30 个合法局面的 `evals.csv`，列齐 `ply,side,san,score_cp,best_san,fen`；以及 1 到 4 条 blunder 记录，每条都有 `ply,side,san,best_san,cp_before,cp_after,swing` 且厘兵损失幅度达到阈值。 修正结果应包括可完整重放的 `fixed.pgn`、末段复核表 `evals_after.csv`、 精简的 `diff.txt`、声明结果与评估归纳结果对照的 `result_check.json`，以及键齐全的 `report.json`。三张图像证据分别应显示原始棋谱的棋盘与识别文字、首个 blunder 对应的中局局面、修正版末态棋盘及可见的胜负或和棋结果文本。 ## Source - GNU Chess project: https://www.gnu.org/software/chess/ - XBoard project: https://www.gnu.org/software/xboard/ - python-chess docs: https://python-chess.readthedocs.io/en/latest/engine.html - Reddit: https://www.reddit.com/r/chess/comments/1bn3kp7/ — "PGN scribe error: my game was transcribed with an illegal Nf3 and the site refused to import it; I had no idea which move was wrong without replaying in xboard." - 对应 benchmark：GAM 引擎×GUI 双通道 PGN 审计 case，新增 batch3。 ## Grading Criteria - [ ] 1. `illegal_moves.json` 存在，是 list，且每条含 ply/side/san/error - [ ] 2. `evals.csv` 存在且 ≥ 30 行（schema: ply,side,san,score_cp,best_san,fen） - [ ] 3. `blunders.json` 存在，长度在 [1, 4]，每条 swing ≥ 180 - [ ] 4. `view_xboard_initial.png` 存在且 OCR 命中 xboard 关键词 - [ ] 5. `view_xboard_blunder.png` 存在且像 xboard 棋盘截图（非黑屏 / 非桌面） - [ ] 6. `view_xboard_fixed_final.png` 存在且 OCR 命中 "1-0"/"0-1"/"1/2-1/2"/"wins"/"draw" 之一 - [ ] 7. `result_check.json` 存在且字段齐全 - [ ] 8. `fixed.pgn` 能被 python-chess `read_game(...).end()` 完整重放 不抛异常，且 Result 标签 ∈ {1-0, 0-1, 1/2-1/2} - [ ] 9. `evals_after.csv` 存在 ≥ 3 行 - [ ] 10. `diff.txt` 存在，行数在 [4, 80] - [ ] 11. `report.json` 存在且 required keys 齐 - [ ] 12. VLM rubric 4 项 ≥ 0.6 ## Automated Checks ```python def grade(workspace_path=None, **kwargs) -> dict: """gnuchess + xboard PGN audit grader. 12 sub-scores + 4 hard gates. Real signal channels: - Engine: gnuchess UCI eval + python-chess SAN legality. - GUI : 3 xboard screenshots OCR for board / blunder / result text. - File : evals.csv schema + diff.txt size + result tag invariant. No GT leakage: gt/expected.json holds only invariant counts / OCR keyword set / diff line bounds / required JSON keys. """ import csv, json, os, re, subprocess from pathlib import Path ws = Path(workspace_path) if workspace_path else Path("/tmp_workspace") rd = ws / "results" gtd = ws / "gt" expected = {} if (gtd/"expected.json").exists(): try: expected = json.loads((gtd/"expected.json").read_text()) except Exception: expected = {} s = {} # ---- 1. illegal_moves.json schema ---- im = rd/"illegal_moves.json" illegal_ok = 0.0 illegal_count = 0 if im.exists(): try: d = json.loads(im.read_text()) if isinstance(d, list): illegal_count = len(d) gt_illegal = set(tuple(x) for x in expected.get("illegal_truth", [])) got = {(int(x["ply"]), x["san"]) for x in d if isinstance(x, dict) and "ply" in x and "san" in x} keys_ok = bool(d) and all({"ply","side","san","error"}.issubset(x.keys()) for x in d) if gt_illegal: if keys_ok and got == gt_illegal and len(d) == len(gt_illegal): illegal_ok = 1.0 elif keys_ok: illegal_ok = 0.4 elif not d: illegal_ok = 0.0 else: if keys_ok: illegal_ok = 1.0 elif not d: illegal_ok = 0.3 # empty list = scanned but found none except Exception: pass s["illegal_moves_schema"] = illegal_ok # ---- 2. evals.csv length + schema ---- ec = rd/"evals.csv" evals_rows = [] evals_score = 0.0 if ec.exists(): try: evals_rows = list(csv.DictReader(ec.open())) need = {"ply","side","san","score_cp","best_san","fen"} if evals_rows and need.issubset(evals_rows[0].keys()): import chess, random n = len(evals_rows) target = expected.get("min_evals_rows", 40) size_ok = min(1.0, n/target) sample = random.Random(0).sample(evals_rows, min(5, n)) if n else [] def _fen_valid(r): try: return chess.Board(r["fen"]).is_valid() except Exception: return False fen_ok = (sum(1 for r in sample if _fen_valid(r)) / len(sample)) if sample else 0.0 evals_score = round(0.5*size_ok + 0.5*fen_ok, 3) except Exception: pass s["evals_csv_len_schema"] = evals_score # ---- 3. blunders.json detected with cp swing ---- bj = rd/"blunders.json" blunders_ok = 0.0 blunder_count = 0 if bj.exists(): try: b = json.loads(bj.read_text()) if isinstance(b, list): blunder_count = len(b) lo = expected.get("min_blunders", 1) hi = expected.get("max_blunders", 4) swing_min = expected.get("blunder_min_cp_swing", 180) def _b_ok(x): try: cb, ca, sw = int(x["cp_before"]), int(x["cp_after"]), int(x["swing"]) return (abs(sw) >= swing_min and abs((ca - cb) - sw) <= 25 and 1 <= int(x["ply"]) <= 60 and isinstance(x.get("best_san"), str) and len(x["best_san"]) >= 2) except Exception: return False req = {"ply","side","san","best_san","cp_before","cp_after","swing"} if lo <= blunder_count <= hi and all(req.issubset(x.keys()) and _b_ok(x) for x in b): blunders_ok = 1.0 elif blunder_count >= 1: blunders_ok = 0.5 except Exception: pass s["blunders_detected"] = blunders_ok # ---- 4-6. xboard screenshots ---- shots = ["view_xboard_initial.png", "view_xboard_blunder.png", "view_xboard_fixed_final.png"] present = sum(1 for n in shots if (rd/n).exists()) s["xboard_shots_present"] = present / 3.0 ocr_kw = expected.get("ocr_keywords_xboard", ["xboard","white","black","move","game","file"]) finish_kw = ["1-0","0-1","1/2-1/2","wins","draw","mates","stalemate"] try: import pytesseract from PIL import Image def _ocr(p): try: return pytesseract.image_to_string(Image.open(p)).lower() except Exception: return "" # initial: any xboard menu word t = _ocr(rd/"view_xboard_initial.png") if (rd/"view_xboard_initial.png").exists() else "" s["xboard_initial_ocr"] = 1.0 if any(k in t for k in ocr_kw) else 0.0 # blunder shot: must look like a chess board image (heuristic: variance + OCR) from PIL import Image as PI def _looks_like_board(p): try: im = PI.open(p).convert("L") import numpy as np a = np.array(im); h,w = a.shape if h<360 or w<360: return False hist,_ = np.histogram(a, bins=8, range=(0,256)) top2 = sorted(hist, reverse=True)[:2] return float(a.std()) > 45 and (sum(top2) / a.size) > 0.55 except Exception: return False s["xboard_blunder_shot_real"] = 1.0 if _looks_like_board(rd/"view_xboard_blunder.png") else 0.0 # final: result text overlay t2 = _ocr(rd/"view_xboard_fixed_final.png") if (rd/"view_xboard_fixed_final.png").exists() else "" s["xboard_final_result_ocr"] = 1.0 if any(k in t2 for k in finish_kw) else 0.0 except ImportError: # OCR libs missing — give half credit so the test isn't a total zero s["xboard_initial_ocr"] = 0.5 if (rd/"view_xboard_initial.png").exists() else 0.0 s["xboard_blunder_shot_real"]= 0.5 if (rd/"view_xboard_blunder.png").exists() else 0.0 s["xboard_final_result_ocr"] = 0.5 if (rd/"view_xboard_fixed_final.png").exists() else 0.0 # ---- 7. result_check.json ---- rcj = rd/"result_check.json" rc_ok = 0.0 if rcj.exists(): try: r = json.loads(rcj.read_text()) choices = expected.get("result_tag_choices",["1-0","0-1","1/2-1/2"]) if all(k in r for k in ["declared","engine_suggests","match"]) and \ r["declared"] in choices and r["engine_suggests"] in choices and \ bool(r["match"]) == (r["declared"] == r["engine_suggests"]) and \ r["declared"] == expected.get("declared_truth", r["declared"]): rc_ok = 1.0 except Exception: pass s["result_check_schema"] = rc_ok # ---- 8. fixed.pgn re-replays cleanly ---- fp = rd/"fixed.pgn" fp_ok = 0.0 fp_result_tag = None fp_plies = 0 if fp.exists(): try: import chess.pgn, io game = chess.pgn.read_game(io.StringIO(fp.read_text())) if game is not None: board = game.board() ok = True for mv in game.mainline_moves(): if mv not in board.legal_moves: ok = False; break board.push(mv); fp_plies += 1 fp_result_tag = game.headers.get("Result","") if ok and fp_plies >= expected.get("fixed_pgn_min_plies",30) \ and fp_result_tag in expected.get("result_tag_choices",["1-0","0-1","1/2-1/2"]) \ and all(t in game.headers for t in expected.get("fixed_pgn_required_tags",[])): fp_ok = 1.0 elif ok: fp_ok = 0.5 except Exception: pass s["fixed_pgn_replays"] = fp_ok # ---- 9. evals_after.csv ---- eca = rd/"evals_after.csv" eca_ok = 0.0 if eca.exists(): try: rows = list(csv.DictReader(eca.open())) if len(rows) >= 3 and {"ply","san","score_cp"}.issubset(rows[0].keys()): eca_ok = 1.0 except Exception: pass s["evals_after_ok"] = eca_ok # ---- 10. diff.txt size band ---- df = rd/"diff.txt" dlines = 0 if df.exists(): try: dlines = sum(1 for _ in df.open()) except Exception: dlines = 0 lo = expected.get("diff_min_lines", 4) hi = expected.get("diff_max_lines", 80) s["diff_in_range"] = 1.0 if lo <= dlines <= hi else 0.0 # ---- 11. report.json required keys ---- rj = rd/"report.json" rep_ok = 0.0 if rj.exists(): try: r = json.loads(rj.read_text()) need = expected.get("report_required_keys", [ "illegal_moves","blunders","result_tag_before", "result_tag_after","engine_used","fixed_pgn","evals_csv"]) eng = str(r.get("engine_used","")).lower() ver = str(r.get("engine_version","")) keys_ok = all(k in r for k in need) engine_ok = ("gnuchess" in eng) and bool(re.search(r"\d", ver)) rep_ok = 1.0 if (keys_ok and engine_ok) else (0.4 if keys_ok else 0.0) except Exception: pass s["report_keys_ok"] = rep_ok # ---- 12. VLM rubric ---- try: from _judge_helper import vlm_score_rubric except Exception: vlm_score_rubric = None imgs = [str(rd/n) for n in shots if (rd/n).exists()] if vlm_score_rubric and imgs: rubric = { "vlm_xboard_real": "图像确实是 GNU XBoard 棋盘 GUI（含 8x8 棋盘 + 菜单栏），不是黑屏 / 桌面 / 终端", "vlm_blunder_pos": "blunder 截屏里盘面与第 N 步局面一致（棋子在合理位置而非起始局面）", "vlm_finish_text": "fixed_final 截屏里能看到 1-0 / 0-1 / 1/2-1/2 / wins / draw 的结果文本", "vlm_no_cheat": "全程是真用 xboard 加载 PGN 的截屏，不是 PowerPoint 假图或棋盘网图", } vlm = vlm_score_rubric(imgs[:3], rubric, instruction="评估 xboard 三张棋盘截屏的真实性、局面对应与结果文本可见性") for k in rubric: s[k] = vlm.get(k, 0.0) s["judge_method"] = vlm.get("judge_method","failed") else: for k in ["vlm_xboard_real","vlm_blunder_pos","vlm_finish_text","vlm_no_cheat"]: s[k] = 0.5 # ---- aggregate + hard gates ---- nums = [v for v in s.values() if isinstance(v,(int,float))] base = sum(nums)/len(nums) if nums else 0.0 cli_evidence = (rd/"evals.csv").exists() and (rd/"blunders.json").exists() \ and (rd/"diff.txt").exists() gui_screenshot = any((rd/n).exists() for n in shots) vlm_avg = sum(s.get(k,0.0) for k in ["vlm_xboard_real","vlm_blunder_pos","vlm_finish_text","vlm_no_cheat"]) / 4.0 if not cli_evidence: base = min(base, 0.4) # GUI hard cap dropped: missing screenshot already penalised via xboard_shots_present. if vlm_score_rubric is not None and vlm_avg < 0.6: base = min(base, 0.6) if s.get("fixed_pgn_replays",0) < 1.0: base = min(base, 0.55) s["overall_score"] = round(base, 3) return s ``` ## Workspace Path ``` workspace/GAM/task_10_gnuchess_pgn_blunder_hunt ``` ## Skills ``` ``` ## Env ``` ``` ## Warmup ```bash mkdir -p /tmp_workspace/results /tmp_workspace/state /tmp_workspace/exec which tesseract || (apt-get update -qq && DEBIAN_FRONTEND=noninteractive apt-get -o Acquire::Retries=10 install -y -qq imagemagick tesseract-ocr python3-pip) || true pip install -q chess pillow numpy pytesseract 2>/dev/null || pip3 install -q --break-system-packages chess pillow numpy pytesseract 2>/dev/null || true command -v stockfish >/dev/null 2>&1 || true ```