mlsanityData to 14 Oct 2026

Certified Corruption Budgets: Anytime-Valid Leaderboard Claims under Adaptive Rigging

arXiv 2610.10597 ↗First posted 6 Oct 2026cs.CR cs.AI stat.MEcited by 0

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Abstract (arXiv)

Public leaderboards for AI models are read continuously, and attackers can see every published standing. Vote rigging, selective disclosure of private variants, and benchmark contamination can each move a ranking. Existing guarantees assume genuine records or bound the corruption per step, which an attacker who corrupts in bursts evades. We introduce the certified corruption budget, a tolerance widehatBt computed after t records and published with each pairwise claim. With probability at least 1-α, simultaneously at all times, the claim is correct or more than widehatBt records were corrupted. It holds against attackers who watch every certificate, with no bound on their budget. Forged records and records altered once seen require different certificates: the certificate for forgeries fails, with probability approaching one, against an attacker who flips votes it has seen, while one that charges roughly twice as much per record remains valid, with constant bets even against attackers who see the future, and no smaller charge is valid at every level. The certified budget grows nearly as fast as any valid method allows: with a win fraction frac12+δ, each new record adds close to 2δ to the number of forged records the claim can withstand (δ flipped). Publishing the best of V private variants costs only an amount growing like log V. In replays on 1.8 million Chatbot Arena votes, a few hundred rigged votes make standard confidence intervals certify false orderings, while ours stays valid. On real votes, our certificate shows that clearly separated models withstand about 2,000 forged votes.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 7 of its 62 references that are in the map. First posted on arXiv 6 Oct 2026.

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7 of 62 references.

References · 7

  1. 2023Instruction-Following Evaluation for Large Language Modelscited by 1,228
  2. 2024Benchmark Data Contamination of Large Language Models: A Surveycited by 147
  3. 2022Holistic Evaluation of Language Modelscited by 131
  4. 2024Admissible online closed testing must employ e-valuescited by 3
  5. 2026A Unified Perturbation Framework for Analyzing Leaderboard Stability and Manipulationcited by 2
  6. 2026Rank Confidence Sequences:Anytime-valid Leaderboardscited by 2
  7. 2026Anytime-Valid LLM Leaderboards via Benchmark-weighted and Block-Factorized e-Processescited by 1

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