Abstract
BACKGROUND: The exponential growth of scientific publications poses a formidable challenge for researchers seeking to validate emerging hypotheses or synthesize existing evidence. In this paper, we introduce Valsci, an open-source, self-hostable utility that automates large-batch scientific claim verification using any OpenAI-compatible large language model. Valsci unites retrieval-augmented generation with structured bibliometric scoring and chain-of-thought prompting, enabling users to efficiently search, evaluate, and summarize evidence from the Semantic Scholar database and other academic sources. Unlike conventional standalone LLMs, which often suffer from hallucinations and unreliable citations, Valsci grounds its analyses in verifiable published findings. A guided prompt-flow approach is employed to generate query expansions, retrieve relevant excerpts, and synthesize coherent, evidence-based reports. RESULTS: Preliminary evaluations across claims from the SciFact benchmark dataset reveal that Valsci significantly outperforms base GPT-4o outputs in citation hallucination rate while maintaining a low misclassification rate. The system is highly scalable, processing hundreds of claims per hour through asynchronous parallelization. CONCLUSIONS: By providing an open and transparent platform for large-batch literature verification, Valsci substantially lowers the barrier to comprehensive evidence-based reviews and fosters a more reproducible research ecosystem.