ABSP: Attention-Based Semantic Preservation for Faithful Context Compression

Counts differ because each database indexes a different set of publications. We treat OpenAlex as the canonical count; Google Scholar is not shown (no API, and crawling it violates its ToS).

Retrieval-augmented generation (RAG) reduces hallucination in large language models by conditioning generation on retrieved evidence, but retrieved contexts are often compressed before inference to reduce cost and latency. Prior work evaluates compression primarily through answer accuracy, leaving open whether different compression strategies affect how faithfully model outputs are grounded in the provided evidence.