mlsanityData to 7 Oct 2026

Can AI Oversight Be Zero Knowledge?

arXiv 2610.01995 ↗First posted 1 Oct 2026cs.AI cs.CC cs.CRcited by 0

Not on the map. Why?

Abstract (arXiv)

AI systems increasingly produce outputs from confidential data, such as a fitness-for-duty assessment from medical records or the predicted properties of a drug candidate from its secret structure. It is important to verify that such outputs are correct without revealing the underlying data. A recent line of work studies verification of AI outputs via interactive proofs and debate for oracle-aided computation, where correctness may depend on an oracle such as human judgment, a physical experiment, or the web. These works focus on verification by a verifier that runs much faster than the computation. However, such efficient verification is impossible for general oracle-aided computation, and these works therefore rely on additional assumptions. We focus instead on privacy: allowing the verifier to run in time polynomial in the computation, we ask whether interactive arguments for oracle-aided computation can be zero knowledge, so that the verifier learns nothing about the confidential data beyond the correctness of the output. We prove that, in general, they cannot. In the random oracle model, there are no zero-knowledge proofs for all oracle-aided computations, even if both the prover and the verifier are allowed to run much longer than the computation itself. The impossibility extends to debate, a canonical model for scalable oversight. On the positive side, we show that if the oracle attaches a cryptographic signature to each of its answers, then every oracle-aided computation can be verified in zero knowledge with an efficient prover and verifier, assuming only collision-resistant hash functions. Beyond privacy, this also gives an alternative approach to scalable oversight that relies neither on an honest opponent, as in debate, nor on the robustness of the computation, as in prior single-prover protocols.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 16 of its 69 references that are in the map. First posted on arXiv 1 Oct 2026.

Builds on

16 of 69 references.

References · 16

  1. 2022Large Language Models Encode Clinical Knowledgecited by 1,807
  2. 2023Weak-to-Strong Generalization: Eliciting Strong Capabilities With Weak Supervisioncited by 445
  3. 2018AI safety via debatecited by 384
  4. 2022Measuring Progress on Scalable Oversight for Large Language Modelscited by 212
  5. 2022Sample Efficiency Matters: A Benchmark for Practical Molecular Optimizationcited by 151
  6. 2018Supervising strong learners by amplifying weak expertscited by 139
  7. 2021PPFL: Privacy-preserving Federated Learning with Trusted Execution Environmentscited by 93
  8. 2023Scalable AI Safety via Doubly-Efficient Debatecited by 54
  9. 2024zkLLM: Zero Knowledge Proofs for Large Language Modelscited by 51
  10. 2021Learning to Give Checkable Answers with Prover-Verifier Gamescited by 29
  11. 2024Trustless Audits without Revealing Data or Modelscited by 14
  12. 2025An alignment safety case sketch based on debatecited by 12
4 more
  1. 2025Avoiding Obfuscation with Prover-Estimator Debatecited by 11
  2. 2024Secure and Confidential Certificates of Online Fairnesscited by 7
  3. 2026Agentic Witnessing: Pragmatic and Scalable TEE-Enabled Privacy-Preserving Auditingcited by 3
  4. 2026Flock: Fast Proving for Batch Boolean Computationscited by 1

Built on by

Not cited yet.