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[2601.22977] Quantifying Model Uniqueness in Heterogeneous AI Ecosystems
Summary
This paper presents a statistical framework for quantifying model uniqueness in heterogeneous AI ecosystems, addressing the challenge of distinguishing genuine novelty from redundancy in AI models.
Why It Matters
As AI systems become more complex, understanding model uniqueness is crucial for governance and trustworthiness. This research introduces a novel approach to audit and quantify uniqueness, which can help improve AI model evaluation and deployment strategies.
Key Takeaways
- Introduces a statistical framework for auditing model uniqueness.
- Establishes that uniqueness is non-identifiable without intervention control.
- Demonstrates the failure of cooperative game-theoretic methods in detecting redundancy.
- Implements the framework across various AI models, enhancing trustworthiness.
- Provides a theoretical foundation for active auditing in AI ecosystems.
Computer Science > Artificial Intelligence arXiv:2601.22977 (cs) [Submitted on 30 Jan 2026 (v1), last revised 12 Feb 2026 (this version, v2)] Title:Quantifying Model Uniqueness in Heterogeneous AI Ecosystems Authors:Lei You View a PDF of the paper titled Quantifying Model Uniqueness in Heterogeneous AI Ecosystems, by Lei You View PDF HTML (experimental) Abstract:As AI systems evolve from isolated predictors into complex, heterogeneous ecosystems of foundation models and specialized adapters, distinguishing genuine behavioral novelty from functional redundancy becomes a critical governance challenge. Here, we introduce a statistical framework for auditing model uniqueness based on In-Silico Quasi-Experimental Design (ISQED). By enforcing matched interventions across models, we isolate intrinsic model identity and quantify uniqueness as the Peer-Inexpressible Residual (PIER), i.e. the component of a target's behavior strictly irreducible to any stochastic convex combination of its peers, with vanishing PIER characterizing when such a routing-based substitution becomes possible. We establish the theoretical foundations of ecosystem auditing through three key contributions. First, we prove a fundamental limitation of observational logs: uniqueness is mathematically non-identifiable without intervention control. Second, we derive a scaling law for active auditing, showing that our adaptive query protocol achieves minimax-optimal sample efficiency ($d\sigma^2\gamma^{-2}\log(Nd/\...
