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Claim 5 source audit
Retrieval
- Paper: Regression Language Models for Code, arXiv:2509.26476.
- Source URL: https://ar5iv.labs.arxiv.org/html/2509.26476
- Retrieval date: 2026-07-27 UTC.
- Request User-Agent:
OpenResearch-Reproduction/1.0 paper-2509.26476. - HTML SHA-256:
5947f4512cc86850a63409adf52af25ac1f40b15dcc797348fd8ae91a2740913. - Anchors: Section 6.2 (
S6.SS2), Table 5 (S6.T5), and Table 6 (S6.T6).
Exact statements and quantifiers
Table 5 is a three-way ablation evaluated on 512 NASBench101 validation samples. The displayed Spearman correlations are 0.478 for a standard regression head, 0.717 for a normalized regression head, and 0.800 for the decoder head. The surrounding text says the training subset contains NASBench101, SNAS, OFA ResNet, OFA ProxylessNAS, and OFA MobileNet. It describes the standard/normalized alternatives as encoder-only four-layer models trained with MSE and the decoder formulation as a two-encoder/two-decoder model trained with cross-entropy.
Table 6 is a two-size comparison evaluated on 1,024 CodeNet samples. It reports 0.744 for T5Gemma s-s prefix-LM (shown as 300M) and 0.782 for T5Gemma b-b prefix-LM (shown as 600M). The surrounding text says both use exactly the same settings and that training uses a smaller subset of CodeNet, APPS, and KernelBook.
Appendix C states the common optimizer family and broad settings: Adafactor, pretraining learning rate 1e-3, fine-tuning learning rate 5e-5, clipping at 2, 10% warmup followed by cosine decay, usual decoder depth 2, hidden size 2048, 8 attention heads, median of 64 inference samples, and maximum input length 2048. These statements do not identify the exact Table 5/6 rows, training steps, seeds, checkpoint-selection rule, complete head implementation, or checkpoints.
Missing identifiers
The exact training and evaluation row IDs are not stated. The smaller subset in Table 6 is not enumerated. The official repository snapshots and author model listing are therefore audited as public evidence, but non-discovery is recorded only as a blocker—not as proof of nonexistence or as falsification.