arXiv (OAI Expanded)
open access
Func-R1: Incentivizing Mathematical Function Reasoning in Multimodal Large Language Models
Performing deliberate mathematical reasoning in visual contexts is a hallmark of advanced Multimodal Large Language Models (MLLMs) and requires a sophisticated synthesis of perceptual grounding and symbolic logic. However, in the realm of mathematical functions, our investigation reveals a critical
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- World Models Meet Language Models: On the Complementarity of Concrete and Abstract Reasoning#798936
- World Models Meet Language Models: On the Complementarity of Concrete and Abstract Reasoning#797522
- TOPS: First-Principles Visual Token Pruning via Constructing Token Optimal Preservation Sets for Efficient MLLM Inference#1063955
- GLaQ: Grounding Latent Queries in Visual Evidence for Multimodal Reasoning#630159
- GLaQ: Grounding Latent Queries in Visual Evidence for Multimodal Reasoning#628297
Record · ID 1084701
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive