arXiv:2606.05382v1 Announce Type: new Abstract: Multi-table question answering requires models to retrieve relevant evidence, link schemas, and perform compositional reasoning across relational tables. Existing multi-table Q&A resources typically provide questions and final answers but lack reasoning supervision that explains how answers are derived. To address this gap, we construct a synthetic contrastive reasoning-trace dataset for MMQA by generating validated positive traces and plausible negative traces with heterogeneous LLMs. We then use the resulting preference pairs to fine-tune open-
The increasing complexity of AI tasks, particularly in multi-modal and multi-table reasoning, is driving demand for more sophisticated training methodologies and datasets.
This development improves AI's ability to perform complex, compositional reasoning, which is crucial for advanced autonomous agents and analytical systems.
AI models will become more capable of understanding and synthesizing information across disparate data sources, reducing the need for explicit human supervision in complex data analysis.
- · AI developers
- · Data analysis platforms
- · Businesses with complex datasets
- · Monolithic AI models
- · Manual data integration specialists
Improved performance of multi-table Q&A systems through enhanced reasoning capabilities.
Acceleration in the development and deployment of more robust AI agents capable of handling diverse information structures.
Potential for new AI-powered applications that leverage complex data synthesis across multiple enterprise systems, automating expert-level reasoning tasks.
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Read at arXiv cs.AI