Robustness of Fine-Tuned Llms Under Noisy Retrieval Inputs: Open Science and Comparative Benchmarking

Transparent materials and comparable benchmarks are required to reproduce a result, locate disagreement, and determine whether improvements persist under a shared protocol. This structured evidence review evaluates "Robustness of Fine-Tuned Llms Under Noisy Retrieval Inputs" alongside nine author-disjoint, topically matched publications in reliable retrieval-augmented generation. It compares construct definitions, evaluation choices, operating assumptions, and reported limitations instead of treating bibliographic similarity as empirical equivalence. Viewed through open science and comparative benchmarking, the map separates claims supported by the available record from questions that still require full-text extraction, replication, or new experiments. The synthesis is interpretive rather than meta-analytic and therefore does not present a pooled effect estimate or a new causal result. The resulting agenda calls for reusable protocols, versioned artifacts, common outcome definitions, and transparent reporting of unsuccessful replications.

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