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Verdict first: the requested comparison is not answerable from the supplied evidence

Neither arXiv 2403-08345 nor arXiv 2410-02811 appears in the supplied evidence records, and "ACMC" (presumably a target system or consortium) is undefined anywhere in the corpus. Any direct comparison would require confabulation, which is refused. What can be battle-tested is the broader claim implied by the framing — that current LLM-KG construction approaches provide transferable guidance for application-specific needs. The supplied records support only qualified, small-N conclusions.

What the supplied LLM-KG evidence actually shows

The strongest comparative signal comes from a controlled three-way extraction study: a fine-tuned decomposed 13B model achieved the highest Graph-RAG QA pass rate (48.4% average, 55.3% peak) versus a 32B general-purpose model (40.3%/46.8%) and base 13B (26.4%/33%), while the base small model produced 179 class violations and 60 relationship violations versus 5/1 for the large model — but fine-tuning and decomposition were introduced together, so their contributions cannot be separated Extraction strategy comparison study. Only six texts, no medical/legal domains, and LLM-as-judge evaluation limit external validity.

Ontology scope matters more than graph size: a distilled step-focused ontology beat a full ontology for step-level QA, and raw triple count did not predict QA accuracy — average node degree and duplication rate did Ontology distillation and graph metric findings. This is a BGPT inference with direct implications for any target system: align the ontology to the downstream task, not to maximal coverage.

LLM-assisted KG integration is feasible but error-prone: an integrated gene-disease-pharmacogenomics graph (16.8k nodes, 83.8k edges) reported ~89.4 precision, ~92.5 coverage, and 91.65 expert validation — yet 15% of GPT-4-suggested edges were rejected by expert review, quantifying hallucination risk LLM-integrated biomedical KG study. Document-structure-informed KG construction (StructuGraphRAG) outperformed generic GraphRAG on a survey codebook, but on a single dataset with no human evaluation protocol detailed Structure-informed KG construction.

Blindspots that would undermine any ACMC mapping

• Reported headline metrics vary in kind (QA pass rate, PageRank matching scores, threshold pass rates) and are not cross-comparable — one multi-agent system reporting 92.3% decision accuracy has no external comparator, no uncertainty intervals, and admits no standardized benchmarks exist Multi-agent RAG system evaluation.
• KG gains often shrink on simpler benchmarks and with small quantized LLMs; 2-hop/3-hop MetaQA gains may be partially template artifacts KG-RAG question decomposition study.
• Drug-repurposing evidence shows disease-specific KGs with Graph-of-Thoughts LLM reasoning beat general KGs on literature-overlap benchmarks — but overlap with a historical 573-drug list is a weak proxy for real efficacy, and nothing validates generalization to new disease domains Graph-of-Thoughts drug repurposing study.
• Pipeline demonstrations on synthetic data (LLMasMMKG, ~10,000 points/domain) show architecture without real-world validation Synthetic multi-modal KG pipeline.

Clinical DDx evidence confirms KG-augmented LLM evaluation (BKG-GPT) can approach clinician agreement (GPT-4 93.33% in Clinician-vs-BKG-GPT scenario) on 50 cases — a promising but tiny, vignette-based signal with probable training-data overlap Lab-data LLM differential diagnosis study.

Confidence note: the negative verdict on the named arXiv comparison is certain (papers absent from evidence); the transferable findings above are moderate-strength, small-N, and would be overturned if ACMC's actual requirements (domain, scale, latency, evaluation criteria) were specified and tested against independent benchmarks.