Relation-free graph retrieval avoids explicit relation extraction, but removing relation labels can weaken shallow ranking when argument order determines which evidence is correct. We test this boundary condition with the official LinearRAG retrieval-only pipeline. On a controlled set of 3,000 questions over nine asymmetric relation types, LinearRAG recovers the gold passage at rank 20 in 96.4% of cases but ranks it first in only 18.2%. Its top-1 output is a same-entity wrong-relation passage in 1,481 cases and a reversed-relation passage in 780 cases, for 2,261 direction errors (75.4%); a further 108 cases are same-relation wrong-entity errors and are reported separately. A local Qwen2.5 reader shows that shallow retrieval errors mainly propagate as abstention or answer failure. The result is deliberately bounded: it diagnoses first-stage retrieval under asymmetric ambiguity, not the strongest possible reranked GraphRAG system. We recommend relation-sensitive shallow-ranking and reader-propagation checks in addition to aggregate recall.

New parameter-efficient fine-tuning (PEFT) methods are often credited with gains against a single default LoRA configuration, even though inexpensive tuning of vanilla LoRA may change the comparison. We present a deliberately small, descriptive case study of two claims: adaptive rank allocation with AdaLoRA and LoRA-specific optimization with LoRA-RITE. Using RoBERTa-base on capped subsets of three GLUE classification tasks, we compare each method with a pre-specified vanilla LoRA control sweep. AdaLoRA beats fixed LoRA at rank 8 in 4 of 7 dataset-seed cells, but beats the best tested fixed rank in only 1 of 7. LoRA-RITE beats default AdamW in 3 of 6 cells, but tuned AdamW in only 1 of 6. Several SST-2 runs collapse to the majority label, so these counts are illustrative rather than population-level estimates. The practical contribution is an attribution protocol: report modest vanilla rank/learning-rate sweeps, per-cell margins, instability checks, and budget accounting before attributing gains to a specialized PEFT mechanism.