arXiv2Table: Toward Realistic Benchmarking and Evaluation for LLM-Based Literature-Review Table Generation

arXiv2Table is a research-oriented project designed to solve a specific bottleneck in LLM-based scientific synthesis: the unrealistic 'oracle' assumptions in existing benchmarks. While its approach to simulating schema-agnostic demands and retrieval noise is technically sound and improves evaluation rigor, it currently lacks the defensive moats required to survive as a standalone entity. With 0 stars and being only 8 days old, it is effectively a reference implementation for an arXiv paper. The core capability it aims to benchmark—multi-document synthesis into structured tables—is a primary target for frontier labs. OpenAI's 'Deep Research' and Google's 'NotebookLM' are already implementing multi-doc reasoning that overlaps significantly with this project's goal. Specialized incumbents like Elicit and Consensus also have massive data moats and existing user bases in this exact niche. The project's value lies in its evaluation methodology for researchers, but as a software tool, it faces immediate displacement by platform-level 'research agent' capabilities.