| """IOL-AI 2026 — v14: v12 (known-good, 0.1233) + DETERMINISTIC exact-match boosters. |
| |
| Base = the EXACT v12 pipeline (v7 prompt + count-fix + CSV-safe single-line explanation, |
| single greedy decode) that scored 0.1233 / EM 0.0609 / explanation_rate 100. v14 adds ONLY |
| post-processing that targets exact_match, scoped to the two task types you named — the core |
| prompt that scored 0.1233 is UNCHANGED for every other task: |
| * text_to_num: model appends a `COMPUTE: e1 | e2` line; we safe-eval it to exact digits |
| (kills arithmetic slips). The COMPUTE instruction is added ONLY for text_to_num rows. |
| * match_letters: `repair_bijection` forces a valid letter permutation in the true bijection |
| case (numbered-context-items == #labels); plus a valid-letters note, ONLY for matching rows. |
| No global prompt change, no self-consistency — so any delta vs v12 is attributable to the boosters. |
| """ |
|
|
| import os |
| os.environ.setdefault("HF_HUB_OFFLINE", "1") |
| os.environ.setdefault("TRANSFORMERS_OFFLINE", "1") |
|
|
| import re |
| import csv |
| import json |
| import ast as _ast |
|
|
| MODEL_DIR = os.environ.get("IOL_MODEL_DIR", ".") |
| TEST_CSV = os.environ.get("IOL_TEST_CSV", "/tmp/data/test.csv") |
| OUT_CSV = os.environ.get("IOL_OUT_CSV", "submission.csv") |
| MAX_NEW_TOKENS = int(os.environ.get("IOL_MAX_NEW_TOKENS", "768")) |
| QUANT = os.environ.get("IOL_QUANT", "4bit") |
|
|
| ANSWER_MARKER = "###ANSWERS###" |
|
|
| |
| SYSTEM_PROMPT = ( |
| "You are an expert competitor at the International Linguistics Olympiad. " |
| "Each problem gives data from a language you have never seen; deduce its rules " |
| "using ONLY the data and hints in the problem, then answer EVERY sub-question.\n\n" |
| "A problem can have MANY sub-questions even when the query is one sentence: e.g. " |
| "'give the correspondences' expects one answer for EACH numbered item in the data " |
| "(often a dozen or more). Work out how many answers are required and give exactly " |
| "that many, one per item, in the order the items appear.\n\n" |
| "Reason briefly, then end your reply with the answers in EXACTLY this format, with " |
| "nothing after it:\n" |
| f"{ANSWER_MARKER}\n" |
| "1. <answer to item 1>\n" |
| "2. <answer to item 2>\n" |
| "(one numbered line per sub-question, in order)\n\n" |
| "Each answer line holds ONLY the requested form — a word, phrase, number, or " |
| "letter — with no restating of the question and no commentary. Answer in the " |
| "language and direction the query asks. For matching items give just the option " |
| "letter; for number items give digits or the written-out number as asked. Never " |
| "leave an item blank — always give your best guess." |
| ) |
|
|
| TASK_HINT = { |
| "translation": "This is a translation task: each answer is only the translated word/phrase.", |
| "text_to_num": "This is a number task: each answer is only digits (e.g. 42).", |
| "num_to_text": "This is a number task: each answer is only the number written in the target language's words.", |
| "match_letters": "This is a matching task: each answer is only the option letter (A, B, C, ...); give one per item in the data.", |
| "matching": "This is a matching task: each answer is only the option letter; give one per item in the data.", |
| "fill_blank": "This is a fill-in-the-blank task: each answer is only the missing form.", |
| "fill_blanks": "This is a fill-in-the-blank task: each answer is only the missing form.", |
| } |
|
|
|
|
| def extract_letter_options(context): |
| """Option labels A,B,C... a matching problem offers (contiguous from A), else None.""" |
| found = set() |
| for line in context.splitlines(): |
| for m in re.finditer(r"(?:^|\s)([A-Z])[.\)]\s+\S", line): |
| found.add(m.group(1)) |
| if not found: |
| return None |
| letters = sorted(found) |
| if letters != [chr(ord("A") + i) for i in range(len(letters))]: |
| return None |
| return letters if 2 <= len(letters) <= 26 else None |
|
|
|
|
| def build_messages(row): |
| """v12 prompt, with a task-scoped booster instruction ONLY for numbers/matching.""" |
| context = (row.get("context") or "").strip() |
| query = (row.get("query") or "").strip() |
| ttype = (row.get("task_type") or "").strip().lower() |
| system = SYSTEM_PROMPT |
| hint = TASK_HINT.get(ttype) |
| if hint: |
| system = system + "\n\n" + hint |
| if ttype == "text_to_num": |
| system += ( |
| "\n\nException to 'nothing after it': after the answer block, add one line:\n" |
| "COMPUTE: expr1 | expr2 | ...\n" |
| "each a plain arithmetic expression (digits, + - *, parentheses only) that " |
| "evaluates to that item's number, one per item, matching the rule you found." |
| ) |
| if ttype in ("match_letters", "matching"): |
| opts = extract_letter_options(context) |
| if opts: |
| system += (f"\n\nThe ONLY valid answers are these letters: {', '.join(opts)}. " |
| "Use no other letter.") |
| return [ |
| {"role": "system", "content": system}, |
| {"role": "user", "content": context + "\n\n" + query}, |
| ] |
|
|
|
|
| def detect_count(context, query): |
| q = re.findall(r"(?m)^\s*(\d+)[\.\)]", query) |
| if q: |
| return len(q) |
| par = re.findall(r"\((\d+)\)", query) |
| if par: |
| return len(set(par)) |
| c = re.findall(r"(?m)^\s*(\d+)[\.\)]", context) |
| if c: |
| return len(c) |
| return 1 |
|
|
|
|
| def _clean_answer(s): |
| s = re.sub(r"^\s*(?:\d+[\.\):]|[-*•])\s*", "", s).strip() |
| s = re.sub(r"^(?:answer|ans|translation|result)s?\s*[:\-]\s*", "", s, flags=re.I).strip() |
| return s.strip("\"'“”‘’` ").strip() |
|
|
|
|
| def parse_answers(text, min_count=1): |
| seg = text.rsplit(ANSWER_MARKER, 1)[1] if ANSWER_MARKER in text else text |
| numbered = {} |
| for m in re.finditer(r"(?m)^\s*(\d+)[\.\)]\s*(.+?)\s*$", seg): |
| numbered[int(m.group(1))] = _clean_answer(m.group(2)) |
| if numbered: |
| answers = [numbered.get(i, "") for i in range(1, max(numbered) + 1)] |
| else: |
| lines = [ln.strip() for ln in seg.splitlines() if ln.strip()] |
| comma_line = next((ln for ln in reversed(lines) if "," in ln), "") |
| if comma_line: |
| answers = [_clean_answer(x) for x in comma_line.split(",")] |
| else: |
| answers = [_clean_answer(ln) for ln in lines] |
| answers = [a if a else "?" for a in answers] |
| if len(answers) < min_count: |
| answers += ["?"] * (min_count - len(answers)) |
| return answers if answers else ["?"] |
|
|
|
|
| def make_explanation(raw, row): |
| """CSV-SAFE single-line explanation from the reasoning (before the answer block).""" |
| head = raw.split(ANSWER_MARKER, 1)[0] if ANSWER_MARKER in raw else raw |
| head = " ".join(head.split())[:300].strip() |
| if head: |
| return head |
| t = (row.get("task_type") or "linguistic").replace("_", " ") |
| return f"Inferred the {t} rule from the given examples and applied it to each item." |
|
|
|
|
| |
| _ALLOWED_BINOPS = (_ast.Add, _ast.Sub, _ast.Mult) |
| _NUM_NODE = getattr(_ast, "Num", None) |
|
|
|
|
| def _safe_arithmetic(expr): |
| try: |
| tree = _ast.parse(expr.strip(), mode="eval") |
| except Exception: |
| return None |
|
|
| def _ev(n): |
| if isinstance(n, _ast.Expression): |
| return _ev(n.body) |
| if isinstance(n, _ast.Constant) and isinstance(n.value, (int, float)): |
| return n.value |
| if _NUM_NODE is not None and isinstance(n, _NUM_NODE): |
| return n.n |
| if isinstance(n, _ast.BinOp) and isinstance(n.op, _ALLOWED_BINOPS): |
| l, r = _ev(n.left), _ev(n.right) |
| if l is None or r is None: |
| return None |
| if isinstance(n.op, _ast.Add): |
| return l + r |
| if isinstance(n.op, _ast.Sub): |
| return l - r |
| return l * r |
| if isinstance(n, _ast.UnaryOp) and isinstance(n.op, _ast.USub): |
| v = _ev(n.operand) |
| return -v if v is not None else None |
| return None |
|
|
| return _ev(tree) |
|
|
|
|
| def apply_compute_overrides(text, answers): |
| m = re.search(r"(?im)^\s*COMPUTE\s*:\s*(.+)$", text) |
| if not m: |
| return answers |
| exprs = [e.strip() for e in m.group(1).split("|")] |
| out = list(answers) |
| for i, e in enumerate(exprs[:len(out)]): |
| v = _safe_arithmetic(e) |
| if v is not None and float(v).is_integer(): |
| out[i] = str(int(v)) |
| return out |
|
|
|
|
| def repair_bijection(answers, labels): |
| n = len(labels) |
| labels_sorted = sorted(labels) |
| picks = [] |
| for i in range(n): |
| a = answers[i] if i < len(answers) else "" |
| f = re.findall(r"[A-Za-z]", a or "") |
| c = f[0].upper() if f else "" |
| picks.append(c if c in labels else "") |
| result = [None] * n |
| used = set() |
| for i in range(n): |
| if picks[i] and picks[i] not in used: |
| result[i] = picks[i] |
| used.add(picks[i]) |
| missing = [l for l in labels_sorted if l not in used] |
| mi = 0 |
| for i in range(n): |
| if result[i] is None: |
| result[i] = missing[mi] if mi < len(missing) else labels_sorted[0] |
| mi += 1 |
| return result |
|
|
|
|
| def postprocess(row, answers, raw): |
| ttype = (row.get("task_type") or "").strip().lower() |
| context = (row.get("context") or "") |
| if ttype == "text_to_num": |
| answers = apply_compute_overrides(raw, answers) |
| elif ttype in ("match_letters", "matching"): |
| labels = extract_letter_options(context) |
| ctx_items = len(re.findall(r"(?m)^\s*\d+\s*[.\)]", context)) |
| if labels and len(labels) >= 2 and ctx_items == len(labels): |
| answers = repair_bijection(answers, labels) |
| return answers |
|
|
|
|
| def _already_quantized(model_dir): |
| cfg = os.path.join(model_dir, "config.json") |
| try: |
| with open(cfg, encoding="utf-8") as f: |
| return "quantization_config" in json.load(f) |
| except Exception: |
| return False |
|
|
|
|
| def load_model(): |
| import torch |
| from transformers import AutoTokenizer, AutoModelForCausalLM |
|
|
| tok = AutoTokenizer.from_pretrained(MODEL_DIR) |
| if tok.pad_token_id is None: |
| tok.pad_token = tok.eos_token |
| if not torch.cuda.is_available(): |
| return tok, AutoModelForCausalLM.from_pretrained( |
| MODEL_DIR, torch_dtype=torch.float32).eval() |
| kwargs = dict(torch_dtype=torch.float16, device_map="auto") |
| if _already_quantized(MODEL_DIR): |
| pass |
| elif QUANT == "4bit": |
| from transformers import BitsAndBytesConfig |
| kwargs["quantization_config"] = BitsAndBytesConfig( |
| load_in_4bit=True, bnb_4bit_compute_dtype=torch.float16, |
| bnb_4bit_quant_type="nf4", bnb_4bit_use_double_quant=True) |
| return tok, AutoModelForCausalLM.from_pretrained(MODEL_DIR, **kwargs).eval() |
|
|
|
|
| def generate_one(tok, model, messages): |
| import torch |
| dev = model.device if hasattr(model, "device") else "cpu" |
| ids = tok.apply_chat_template( |
| messages, add_generation_prompt=True, return_tensors="pt").to(dev) |
| with torch.no_grad(): |
| gen = model.generate(ids, max_new_tokens=MAX_NEW_TOKENS, do_sample=False, |
| pad_token_id=tok.pad_token_id) |
| return tok.decode(gen[0][ids.shape[-1]:], skip_special_tokens=True).strip() |
|
|
|
|
| def main(): |
| tok, model = load_model() |
| with open(TEST_CSV, newline="", encoding="utf-8") as f: |
| rows = list(csv.DictReader(f)) |
|
|
| fout = open(OUT_CSV, "w", newline="", encoding="utf-8") |
| writer = csv.DictWriter(fout, fieldnames=["id", "pred", "explanation"]) |
| writer.writeheader() |
| fout.flush() |
|
|
| for k, r in enumerate(rows): |
| context = (r.get("context") or "").strip() |
| query = (r.get("query") or "").strip() |
| min_count = detect_count(context, query) |
| try: |
| raw = generate_one(tok, model, build_messages(r)) |
| answers = postprocess(r, parse_answers(raw, min_count), raw) |
| explanation = make_explanation(raw, r) |
| except Exception as e: |
| print("row %s fallback: %r" % (r.get("id"), e), flush=True) |
| answers = ["?"] * min_count |
| explanation = "Answer derived from the patterns in the examples." |
| writer.writerow({"id": r["id"], |
| "pred": json.dumps(answers, ensure_ascii=False), |
| "explanation": explanation}) |
| fout.flush() |
| print("%d/%d done" % (k + 1, len(rows)), flush=True) |
|
|
| fout.close() |
| print("wrote %s (%d rows)" % (OUT_CSV, len(rows)), flush=True) |
|
|
|
|
| if __name__ == "__main__": |
| main() |
|
|
