CoSPlay: Cooperative Self-Play at Test-Time with Self-Generated Code and Unit Test

# CoSPlay 핵심 로직 스케치 (Python pseudo-code)

def cosplay(problem: str, llm, Nc=16, Nt=16, Tmax=5, R=16):
    # Stage 1: 아이디어 탐색
    hints = llm.generate(f"Generate high-level solution hints for: {problem}")
    plans = [llm.generate(f"Expand plan for hint: {h}") for h in hints]
    attack_ideas = [llm.generate(f"What edge cases / failure modes for plan: {p}?") for p in plans]

    # 코드 & UT 풀 초기화
    codes = [llm.generate_code(problem, plan=p) for p in plans[:Nc]]
    uts = []
    for i in range(Nt):
        if i < Nt // 2:
            inp = llm.generate(f"Generate random valid input for: {problem}")
        else:
            inp = llm.generate(f"Generate input targeting failure: {attack_ideas[i % len(attack_ideas)]}")
        # 자기 일관성 필터: 4번 샘플 중 3번 이상 동의하면 채택
        outputs = [llm.solve(problem, inp) for _ in range(4)]
        if outputs.count(max(set(outputs), key=outputs.count)) >= 3:
            uts.append((inp, max(set(outputs), key=outputs.count)))

    # Stage 2: Self-Play 반복
    for _ in range(Tmax):
        M = [[execute(c, ut[0]) == ut[1] for ut in uts] for c in codes]
        code_pass = [sum(row) for row in M]
        ut_pass = [sum(M[i][j] for i in range(len(codes))) for j in range(len(uts))]

        # Step 1: 0 pass 코드 제거 후 재샘플
        codes = [llm.generate_code(problem) if code_pass[i] == 0 else codes[i]
                 for i in range(len(codes))]

        # Step 2: 가장 낮은 non-trivial UT 교체 (spurious coupling 제거)
        # Step 3: 가장 신뢰도 높은 non-trivial UT로 실패 코드 수정
        # Step 4: 0% / 100% pass UT 교체
        # ... (각 스텝 후 M 재계산)

    # Stage 3: 클러스터 선택
    top_codes = [c for i, c in enumerate(codes) if code_pass[i] == max(code_pass)]
    random_inputs = [llm.generate(f"Random valid input: {problem}") for _ in range(R)]
    signatures = {c: tuple(execute(c, z) for z in random_inputs) for c in top_codes}

    clusters = {}  # signature -> list of codes
    for c, sig in signatures.items():
        placed = False
        for key in clusters:
            if all(sig[k] == key[k] or sig[k] == 'ERR' or key[k] == 'ERR'
                   for k in range(R)):
                clusters[key].append(c)
                placed = True
                break
        if not placed:
            clusters[sig] = [c]

    best_cluster = max(clusters.values(), key=len)
    return best_cluster[0]