arXiv (All)
open access
ActICL: Post-Training for Agentic Multimodal In-Context Learning
Multimodal in-context learning (ICL) is an important capability of multimodal large language models (MLLMs). Its performance is closely tied to the demonstrations it uses, making demonstration retrieval a core problem for ICL. Common methods use fixed scoring methods to retrieve all the demonstratio
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
- T2T-VICL: Cross-Task Visual In-Context Learning via Implicit Text-Driven VLMs#1110139
- Towards In-Parameter Memory Augmentation for Large Language Models#1218422
- SkillApt: Learning When to Activate Agent Skills from Counterfactual Evidence#1063074
- MultivationBench: A Benchmark for Multimodal Sequential Motivation Reasoning#793807
- MultivationBench: A Benchmark for Multimodal Sequential Motivation Reasoning#789802
Record · ID 1345298
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