pith. sign in

arxiv: 2504.10861 · v2 · pith:TZGDTYO3new · submitted 2025-04-15 · 💻 cs.CL

Ai2 Scholar QA: Organized Literature Synthesis with Attribution

classification 💻 cs.CL
keywords scholarscientificansweringliteraturepublicsystemsaccessiblealong
0
0 comments X
read the original abstract

Retrieval-augmented generation is increasingly effective in answering scientific questions from literature, but many state-of-the-art systems are expensive and closed-source. We introduce Ai2 Scholar QA, a free online scientific question answering application. To facilitate research, we make our entire pipeline public: as a customizable open-source Python package and interactive web app, along with paper indexes accessible through public APIs and downloadable datasets. We describe our system in detail and present experiments analyzing its key design decisions. In an evaluation on a recent scientific QA benchmark, we find that Ai2 Scholar QA outperforms competing systems.

This paper has not been read by Pith yet.

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Forward citations

Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. Rethinking Reasoning-Intensive Retrieval: Evaluating and Advancing Retrievers in Agentic Search Systems

    cs.CL 2026-05 unverdicted novelty 7.0

    BRIGHT-Pro and RTriever-Synth advance reasoning-intensive retrieval by adding multi-aspect evidence evaluation and aspect-decomposed synthetic training, with the fine-tuned RTriever-4B showing gains over its base model.

  2. When More Cores Hurts: The Vector Database Scaling Paradox in HPC

    cs.DC 2026-06 unverdicted novelty 6.0

    Large-scale HPC evaluation of Qdrant, Milvus, and Weaviate reveals that workload patterns limit scaling and extra cores can reduce throughput, exposing a cloud-to-HPC design mismatch.