arXiv subjects

Yingzhe Yu

Publications and source records attributed to Yingzhe Yu.

3 recordsLinked to original sources

arXiv2026

Repeated-Game Security for Restaking-Based Verifiable Inference

Restaking-based protocols enable verifiable LLM inference without the high proving cost of zkML or the hardware trust assumptions of TEEs. Their security is commonly justified by a one-round slashing condition: a rational provider should not cheat when the expected penalty exceeds the cost saving from dishonest inference. This paper shows that this condition can overstate security when inference is supplied repeatedly under the same stake. We model verifiable inference as a discounted repeated game and identify a repeated-game gap caused by proportional slashing: detected deviations reduce future penalty exposure, while cost savings are earned again across queries. We derive the gap in closed form, show that it persists under minimum-stake ejection, and extend it to memoryless bounded-slashing protocols covering deployed designs. We propose a deployable mechanism combining history-dependent challenges, reputation-weighted slashing, and stake vesting. The mechanism restores infinite-horizon subgame-perfect incentive compatibility against stationary mixed-strategy deviations above an explicit discount-factor threshold without per-query cryptographic verification. Evaluation across nine open-weight model pairs from 0.5B to 14B parameters shows that the audit signal has the required concave detectability response. A Stackelberg audit-budget analysis shows that improved signal responsiveness reduces the baseline audit rate by 2.6x at discount factor 0.95. Calibrated to deployed parameters, surveyed protocols pass one-round incentive compatibility but admit repeated-game deviations for discount factors between 0.92 and 0.98, with deviation-profit fractions of 1.5%--8%. Our mechanism reduces deviation profits by 31%--54% while maintaining low-latency economic verification.

cs.GT↗

arXiv2026

Fine-Tuning Integrity for Modern Neural Networks: Structured Drift Proofs via Norm, Rank, and Sparsity Certificates

Fine-tuning is the dominant paradigm for adapting large machine learning models, yet current deployment pipelines provide no way to verify how a released model was updated. In particular, a model provider or auditor cannot check whether a fine-tuned model adheres to a claimed update procedure without access to its parameters. We introduce \emph{fine-tuning integrity} (FTI), a cryptographic objective for verifying that a deployed model differs from a trusted base model only within a declared class of admissible updates. We construct \emph{succinct model difference proofs} (SMDPs), zero-knowledge protocols that certify structured parameter drift without revealing model weights. Our framework supports three fundamental update classes: norm-bounded, low-rank, and sparse drift, covering common fine-tuning methods such as regularized training, LoRA, and prefix tuning. In all cases, proof size and verification cost depend on the structure of the update rather than the number of parameters. We prove soundness, zero-knowledge, and succinctness for each construction, and establish a matching $\Omega(n)$ lower bound showing that structural assumptions are necessary for succinct verification. A prototype evaluation on synthetic benchmarks and GPT-2 fine-tuning demonstrates that proofs remain compact and verification is efficient at realistic scales.

cs.CR↗

arXiv2026

RegGuard: Legitimacy and Fairness Enforcement for Optimistic Rollups

Optimistic rollups provide scalable smart-contract execution but remain unsuitable for regulated financial applications due to three structural gaps: semantic legitimacy, cross-layer state consistency, and ordering fairness. We introduce RegGuard, a unified framework that enhances optimistic rollups with comprehensive legitimacy guarantees. RegGuard integrates three coordinated mechanisms: a decidable semantic validator powered by the RegSpec rule language for encoding regulatory constraints; a cross-layer state pre-synchronization validator that detects inconsistent L1-L2 dependencies with probabilistic reliability bounds; and a cryptographically verifiable fair-ordering service that ensures transaction sequencing fairness with negligible violation probability. We implement a 15,000-line prototype integrated into an Optimism-based rollup and evaluate it under adversarial conditions. RegGuard reduces settlement failures by over 90%, prevents detectable ordering manipulation, and maintains 85% of baseline throughput.

cs.CR↗