arXiv (All)
open access
Can Linguistic Reasoning Vectors Enhance Multimodal Reasoning Ability?
Most Vision-Language Models (VLMs) are built by extending pretrained Large Language Models (LLMs) with visual modules and multimodal alignment. However, this multimodal scaling often degrades the language-side reasoning ability originally encoded in the base LLM. While the base LLM retains usable re
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
- Dual-Latent Memory Routing for Vision-Language Reasoning#972600
- Distilling Visual Reasoning into Text Space#1128679
- SinkPruner: Sink-Free Visual Token Pruning for Multimodal Large Language Models#820996
- Inductive Visual Logic for Few-Shot Out-Of-Distribution Adaptation in VLMs#1142963
- Mind What Matters for Reasoning: Aligning Cross-Modal Attention via Selective Probability Mass Concentration#1078011
Record · ID 1105698
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