💻 computer science

Auditable Release Assurance for Quantized LLM Deployment Configurations: Anytime-Valid Non-Inferiority and Executable Certificates

This paper introduces an auditable, anytime-valid statistical framework with executable certificates to rigorously decide whether quantized large language models are non-inferior to reference configurations across specific deployment environments, thereby controlling false-certification risks while supporting flexible, configuration-specific release assurance.

Original authors: Myunglae Jo, Donghyun Kim, Changhan Son, Minseong Park, Il-Hyuk Yu, Hyungkwang Lim

Published 2026-11-11

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CC BY 4.0

Original authors: Myunglae Jo, Donghyun Kim, Changhan Son, Minseong Park, Il-Hyuk Yu, Hyungkwang Lim

Original paper licensed under CC BY 4.0 (https://creativecommons.org/licenses/by/4.0/). ✨ This is an AI-generated explanation of the paper below. It is not written or endorsed by the authors. For technical accuracy, refer to the original paper. Read full disclaimer

In the world of modern artificial intelligence, a large language model is often treated like a single, monolithic brain. However, to make these powerful systems run on everyday devices like laptops or smartphones, engineers must shrink them down. This process, known as quantization, compresses the model's massive internal data to save space and speed up calculations. But compression is a risky trade-off. Just as a photograph loses detail when compressed into a smaller file, a compressed AI model might lose the ability to answer questions correctly or follow instructions precisely. The core challenge for developers is knowing exactly when a compressed version is good enough to replace the original. If they release a model that is slightly worse, the consequences can range from frustrating user errors to critical failures in safety-sensitive applications. The industry has long relied on simple averages to make these decisions, but averages can hide dangerous flaws, masking the fact that a model might fail spectacularly on specific, important tasks even if its overall score looks acceptable.

A team of researchers has developed a new, rigorous method to solve this problem, creating a digital "quality gate" that acts as a certified seal of approval for compressed AI models. Instead of relying on a single test score, their approach treats the release of a model as a formal software-quality decision that must be auditable and mathematically proven. They designed a system that compares a compressed model against its original, uncompressed version item by item. Rather than just asking, "Is the average score lower?", the system asks a more precise question: "Is the number of mistakes on specific tasks higher than a pre-agreed limit?" This limit, or margin of error, is set by the developers before the testing begins. The system then runs a statistical check that allows them to stop testing early if the model is clearly safe, or continue testing until they are absolutely certain, without ever inflating the risk of a false approval.

The researchers tested this method on twelve different scenarios involving real-world models running on actual hardware, including NVIDIA Jetson devices and Apple Mac computers. They found that the old way of testing often missed subtle but significant problems. For instance, a compressed model might get the same average accuracy as the original, but in reality, it might be getting completely different answers on nearly twenty percent of the questions. The new method catches these "flips" in correctness, ensuring that the compressed model behaves consistently with the original. In their simulations, the new system successfully controlled the rate of false approvals, keeping the risk of certifying a bad model below five percent, even when the testing was stopped early or repeated multiple times. This level of control is crucial because it prevents teams from "gaming" the system by running tests over and over until they get a lucky result that looks good.

A critical discovery in this work is that a compressed model's performance is not just about the model file itself; it is deeply tied to the specific environment where it runs. The researchers found that the exact same compressed model file could behave very differently depending on whether it was running on a Mac or an NVIDIA device, or even on different versions of the software engine powering it. In one striking case, the same model file produced different correct answers on only eighty-eight percent of the questions when moved from one machine to another. This means that a certificate of quality issued for a model on one computer cannot be trusted on another. The researchers' solution binds the quality certificate to the entire "deployment configuration"—the specific combination of the model file, the hardware, the software engine, and the settings used. This ensures that the guarantee is specific to the exact setup being used, rather than a vague promise about the model in general.

The system also introduces a "lifetime ledger" to manage risk over time. In a typical development cycle, engineers might test many different versions of a model, discarding the ones that look bad and keeping the ones that look good. This process of selection can accidentally inflate the risk of releasing a flawed model, as the team might just be picking the luckiest test result. The new protocol accounts for this by dividing the total allowed risk budget among all the candidates tested and all the releases made over the life of the project. If a team tests eight different versions, the system ensures that the chance of falsely approving a bad one remains low, regardless of how many times they try. This creates a transparent record where every decision to release a model is backed by a signed, replayable certificate that an independent third party can verify.

Ultimately, this work provides a practical, end-to-end workflow for the safe deployment of compressed AI. It moves the industry away from guesswork and simple averages toward a system of verified, auditable evidence. By proving that a compressed model is not significantly worse than the original within a defined margin, and by binding that proof to the specific hardware and software it runs on, the researchers have created a reliable standard for software quality. The method does not claim that the compressed model is perfect or that it will work for every possible task; rather, it provides a clear, bounded guarantee that the model is safe to use for the specific purposes it was tested on. This approach allows developers to confidently shrink their AI models for edge devices, knowing that the trade-offs have been measured, recorded, and certified with mathematical precision.

Technical Summary: Auditable Release Assurance for Quantized LLM Deployment Configurations

1. Problem Statement

Deploying a quantized Large Language Model (LLM) is not merely a model-weight update but a complex software-quality release decision involving the entire deployment configuration (weights, quantization format, inference engine, runtime libraries, hardware, tokenizer, and generation controls). Current release practices often rely on single-point benchmark comparisons (e.g., average accuracy gaps), which fail to control the false-certification rate under adaptive evaluation, optional stopping, or repeated testing.

The core problem is the lack of a bounded, auditable gate to decide whether a candidate quantized configuration ( $q$ ) can replace a reference configuration ( $b$ ) without exceeding a declared non-inferiority margin ( $\epsilon$ ) and a false-certification budget ( $\delta$ ). Existing heuristics do not account for:

Optional Stopping: Teams iterating on benchmarks until results look acceptable, inflating false-positive rates.
Configuration Specificity: The same model artifact may behave differently across hardware, engine builds, or quantization backends.
Item-Level Discordance: Aggregate accuracy changes can mask significant item-level correctness flips (e.g., a net 0% change hiding 10% of items flipping from correct to incorrect and vice versa).

2. Methodology and Protocol

The paper proposes Protocol 3.1, an end-to-end software-quality release workflow that combines statistical rigor with executable evidence binding.

2.1 Problem Formalization

Deployment Configuration (DCP): Defined as a tuple $C = (M, Q, E, S, H, I, G, V)$ covering model, quantization, engine, runtime, hardware, tokenizer, generation parameters, and evaluator.
Non-Inferiority Certification: The null hypothesis is $H_0: \Delta \ge \epsilon$ (the quantized model is inferior beyond tolerance). Certification requires rejecting $H_0$ at level $\delta$ .
Paired Outcomes: The method uses paired item-level outcomes $d(x) = m(b(x)) - m(q(x)) \in \{-1, 0, 1\}$ , where $-1$ is a beneficial flip, $0$ is no change, and $1$ is a harmful flip. This paired design reduces variance compared to unpaired tests.

2.2 Two Sampling Regimes

The protocol supports two distinct estimands, each with specific validity guarantees:

R1 (Superpopulation): Items are drawn i.i.d. from a task distribution $D$ $D$ . The goal is to certify the expected degradation $\Delta = E_D[d(x)]$ $Δ = E_{D} [d (x)]$ for future requests.
- Method: Uses an e-process (wealth process) based on betting against the null. It supports anytime-valid inference, allowing the auditor to stop at any data-dependent time while maintaining the $\delta$ guarantee via Ville's inequality.
R2 (Fixed Audit Frame): The target is the mean degradation $\bar{\Delta}_N$ $\overset{ˉ}{Δ}_{N}$ over a committed, finite set of $N$ $N$ items inspected in a pre-committed, uniformly random order without replacement.
- Method: Uses a conditional e-process that bets against the conditional null mean of the remaining items. It includes a terminal census rule: if the wealth process does not cross the threshold by $t=N$ , the entire frame is evaluated, and the decision is deterministic based on the exact frame mean. This guarantees certification for all truly non-inferior finite populations by $t=N$ .

2.3 Executable Assurance and Binding

To ensure auditability and prevent "margin shopping" or hidden candidate selection:

Signed Certificates: The output is a signed JSON document binding the deployment profiles, task/evaluator manifests, audit frame, selection rules, and the ordered per-item records.
Pre-commitment: The inspection order must be fixed before outcomes are observed. For adaptive selection (e.g., choosing the best of $K$ candidates), the selection rule and evidence must be fixed before the audit permutation is generated (using public randomness or a deterministic seed).
Replayability: An independent third party can run a verifier to recompute the wealth process, confidence sequences, and the final decision from the provided files.
Risk Allocation: The protocol implements a lifetime risk ledger ( $\sum \delta_r \le \delta_{lifetime}$ ) and handles candidate selection via Bonferroni correction ( $\delta/K$ ) or holdout strategies to prevent risk inflation.

3. Key Contributions

Software-Quality Release Criterion: A formal definition for certifying a full deployment configuration (not just weights) against a reference under specific tolerance ( $\epsilon$ ) and risk ( $\delta$ ) constraints for both superpopulation and finite-frame targets.
Anytime-Valid Statistical Protocol: A paired non-inferiority protocol using e-processes that controls false certification under optional stopping (R1) and without-replacement inspection (R2), including a terminal census rule for finite frames.
Executable Assurance Evidence: A signed, replayable certificate format that binds the decision to specific configuration fingerprints, task files, and randomization seeds, enabling independent verification of the entire release logic.
Empirical Validation: Evaluation on twelve measured model-task-quantization populations across NVIDIA Jetson Orin Nano and Mac configurations, demonstrating that observed false-certification rates align with stated budgets.
Configuration-Specific Evidence: Empirical demonstration that identical model artifacts can produce materially different outcomes across machine/build configurations (up to 11.8% disagreement), necessitating certificates bound to configurations rather than just model files.

4. Results and Observations

Error Rate Control: In simulations across the null region ( $\Delta \ge \epsilon$ ), the maximum observed false-certification rate was 3.8% for a 5% budget, consistent with theoretical guarantees.
Efficiency: The paired design significantly reduces variance. On measured populations, the paired-difference variance was 2.4–78 times smaller than unpaired two-sample variance.
Early Stopping: For clearly non-inferior configurations, the R2 protocol often stopped early (e.g., median 167 items for a 500-item frame). However, for configurations near the boundary (e.g., degradation 2.8% vs. $\epsilon=3\%$ ), the process often required exhausting the entire frame to reach a decision.
Discordance vs. Net Degradation: The study found that net accuracy changes can be small while item-level discordance is high (e.g., 21% of items flipping correctness status with a net change of only 2.7%). The protocol accounts for this via the paired structure.
Configuration Sensitivity:
- Backend Interaction: Within a single machine, changing the inference backend (CUDA vs. CPU) had negligible effect on precision (interaction effect -0.2pp).
- Machine/Build Sensitivity: Changing the machine or engine build caused significant shifts (e.g., 11.8% prediction disagreement between Jetson and Mac builds of the same artifact). This validates the requirement for certificates to bind to specific hardware and build configurations.
Resource Trade-offs: Quantization (e.g., Q8, Q4) provided significant throughput speedups (1.6x–3.4x) and file size reductions (1.9x–3.1x) on edge devices, with measurable, certifiable quality costs.

5. Significance and Claims

The authors explicitly state that they do not claim novelty for the individual statistical components (paired tests, non-inferiority testing, e-processes, or quantization evaluation) in isolation.

The paper's primary contribution is the end-to-end composition of these elements into a software-quality release workflow. This includes:

A one-sided release criterion placing the burden of proof on the compressed model.
Explicit control of optional stopping and candidate selection.
Configuration and evidence binding to prevent "margin shopping" and ensure reproducibility.
A re-certification lifecycle that handles drift and configuration changes.

The work provides a relative release evidence mechanism. It certifies that a specific configuration is non-inferior to a specific reference under a specific margin and budget. It explicitly does not claim to be an absolute task-suitability certificate, a production-readiness guarantee, or a proof of safety against adversarial inputs. The validity of the certificate is strictly limited to the recorded deployment configuration and the committed task distribution or audit frame.