arXiv 论文提出开放式前沿挑战赛的防女巫攻击支付机制
Who Moved the Frontier? Paying Open Frontier Challenges with Free Identities
刷榜这事在开放挑战赛里真会发生，论文复现时一个选手开两个号就包揽前两名。作者把预测市场付费规则证明了一遍，还给出按次收费和版税两种补救方案。如果你在做开放基准或众包评测，这篇直接帮你避开坑。
一篇 arXiv 论文（2611.14974）研究开放式前沿挑战赛中的奖励支付问题：任何人类或 AI 智能体都可以向共享问题提交改进，成绩超过现有最优者即成为新基线，而身份免费注册导致排名奖励可被刷榜。作者在复现某挑战赛一条赛道时发现，一个求解者用两个身份提交即可同时拿下第一和第二名。论文分析了一种预测市场式规则，为每个被接受的步骤按其新增进展付费：在精确分数下，仅看步骤起点与终点的规则中只有这一类能抵抗拆分与合并攻击，多账号无利可图。
在带噪声的分数下，论文证明任何“只付不收回”且为真实改进付费的机制，平均而言也会为无实际作用的改动付钱。作者提出按次提交收费方案，若验证者诚实且提交噪声不超预期，可消除每次尝试的期望利润；但该噪声上界无法被强制执行，总支出没有上限。论文还提出“版税”机制，衡量移除早期改动后后期收益的下降幅度，无需声明即可归属贡献，但要求改动可被移除。作者基于五个公开挑战赛，指出了各自结果支持的规则。
该研究将女巫攻击、贡献归属与机制设计纳入开源竞赛平台的核心问题，对运营开放基准和众包挑战的组织者有直接参考价值。
Who Moved the Frontier? Paying Open Frontier Challenges with Free Identities
In an open frontier challenge, anyone, person or AI agent, submits changes to a shared problem, and a change that beats the best measured score becomes the public baseline. How should it pay when identities are free? Rank prizes can be gamed: on a replay of one track of a recent challenge, one solver's steps under two identities would have taken first and second prize. We consider a prediction-market rule, proposed for machine-learning contests, that pays each accepted step for the progress it adds; with exact scores, among rules that see only a step's start and end, only these resist splitting and merging, and extra identities gain nothing. With noisy scores and independent normal runs, we prove that any payment that never takes money back and pays real improvements also pays something, on average, to a change that does nothing. We propose a fee per submission, at least what luck pays per attempt, that removes each attempt's expected profit if verifiers are honest and no submission is noisier than planned; nothing enforces that bound, and under noise the rule does not cap the total paid. If crediting another and faking credit to one's second account look the same, a rule that neither rewards fakes nor punishes honesty pays for credit at most what lying costs. We introduce a royalty on how much a later gain falls without the earlier change, which needs no claims but needs removable changes. For five public challenges, we name the rule our results would support.
本条情报归属 AITOP「论文」核心追踪栏目。在跨区域产业观察坐标中，该动态直接关联「AI 开发者工具链、Coding Agent 与工程化实践」研究主线，反映出当前技术演进与商业落地的最新风向。涉及核心实体与技术议题包括：女巫攻击、机制设计、预测市场、众包竞赛，读者可通过下方标签进入对应专题页查阅历史脉络与关联对比。
信源核验说明：本文原始线索采编自「arXiv cs.LG」（@Luca Zanolini, Duc V. Le），经 AITOP 多维评分管线结构化提炼。