paper_id	claim	claim_status	evidence_status	source_depth	source_quote	page_or_section	taxonomy_fit	audit_status
2506.11042v2	We propose GenFT, a W0-conditioned PEFT framework that generates task-specific updates through row and column transformations, improving adaptation across NLP and CV tasks.	preliminary-linked	has evidence row	full-text	Nevertheless, GenFT achieves the best average score (85.87%) with only 0.24M parameters, outperforming LoRA and other baselines on the remaining tasks.	4	in-scope: LLM extractor confirmed direction match	needs work; filled but source-depth unclear; report=audit_report.md
2606.04325v1	Learnable Rank LoRA (LR-LoRA): a parameter-efficient fine-tuning method that learns layer-wise adapter ranks during training, introducing a more flexible inductive bias for adaptation.	supported	has evidence row	full-text	Compared to RandLoRA [Albert et al., 2025a], the strongest non-adaptive-rank PEFT baseline at this scale, LR-LoRA improves by+1.57to+4.68 points (Phi-3 15k: +1.57; Qwen2 15k/170k: +2.99/+3.19; Phi-3 170k: +2.08; LLaMA3 15k/170k: +4.68/+2.63), and consistently exceeds the recent adaptive-rank baselines.	1	in-scope: LLM extractor confirmed direction match	needs work; full-text verified; report=audit_report.md
2605.08177v1	We introduce Echo-LoRA, a training-time cross-layer injection mechanism that feeds answer-boundary representations from deeper layers into shallow LoRA/DoRA adaptation modules.	supported	has evidence row	full-text	On eight commonsense reasoning benchmarks, Echo-LoRA exceeds the reported LoRA baselines by 5.7 percentage points on average across LLaMA-7B, LLaMA2-7B, and LLaMA3-8B.	1	in-scope: LLM extractor confirmed direction match	needs work; full-text verified; report=audit_report.md
2511.21285v3	We introduce the PEFT-Bench, an end-to-end benchmark that defines the datasets, metrics, and methodology for evaluating PEFT methods in NLP in a fair and consistent environment.	supported	has evidence row	full-text	We simultaneously in- troduce thePEFT-Factory framework2(Belanec et al., 2025b), which provides a necessary under- lying technological support for execution of the PEFT-Bench benchmark.	Abstract	in-scope: LLM extractor confirmed direction match	needs work; full-text verified; report=audit_report.md
2501.13787v1	This survey aims to provide a comprehensive overview of PEFT techniques applied to diverse FMs and address critical gaps in understanding the techniques, trends, and applications.	preliminary-linked	has evidence row	full-text	Taking GPT-3 [3] as an example, full fine-tuning involves all 175B parameters, whereas LoRA [36] requires training only 4.7M or 37.7M, saving over 99.97% of parameters, and the result is a 0.1% to 0.5% improvement compared to full fine-tuning.	2	in-scope: LLM extractor confirmed direction match	needs work; filled but source-depth unclear; report=audit_report.md