[2608.20290] Phantom Gains: Auditing Self-Improvement Against a Measured Null

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arXiv:2608.20290 (cs)

[Submitted on 20 Aug 2026]

Title:Phantom Gains: Auditing Self-Improvement Against a Measured Null

Authors:Cheng Xu, Nan Yan, Liming Chen, M-Tahar Kechadi<br>View a PDF of the paper titled Phantom Gains: Auditing Self-Improvement Against a Measured Null, by Cheng Xu and Nan Yan and Liming Chen and M-Tahar Kechadi

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Abstract:Whether a language model has improved itself is increasingly judged not by mean accuracy but by which individual problems it gains and loses. Tracking these transitions means differencing two noisy estimates, leaving them vulnerable to measurement artifacts. Auditing three rounds of rank-$32$ LoRA self-training on Qwen3-8B against a frozen control pushed through the identical pipeline, we identify seven measurement failures, each of which inverts a reported finding when its control is absent. Several are standard practice. A ledger built on a single greedy decode manufactures capability changes on an untrained model, largely an artifact of inference batching; the expansion statistic separating acquisition from sharpening assigns that same model a rate of $0.280$. The natural threshold repair does not survive replication: estimated across the frozen comparisons such a design already contains, its null stays non-zero. We replace it with a per-problem exact test against a pooled baseline under false-discovery-rate control, which detects nothing on any held-out replicate and is unchanged under the multiple-testing rule, error rate and pool size. Applied to a ladder of arms matched in stream, volume and evaluation, the audit finds that external distillation improves problems the base model rarely reaches while three forms of self-training do not; a regression rejects this asymmetry as a by-product of distillation's larger overall gain ($p

Comments:<br>Code and evaluation artifacts are available at this https URL

Subjects:

Artificial Intelligence (cs.AI); Computation and Language (cs.CL)

Cite as:<br>arXiv:2608.20290 [cs.AI]

(or<br>arXiv:2608.20290v1 [cs.AI] for this version)

https://doi.org/10.48550/arXiv.2608.20290

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arXiv-issued DOI via DataCite (pending registration)

Submission history<br>From: Cheng Xu [view email]<br>[v1]<br>Thu, 20 Aug 2026 17:30:14 UTC (285 KB)

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