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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. | |