arXiv (OAI Expanded)
open access
OmniHarness: Harnessing Generalizable Visual Generation via Symbolic Policy Learning
Unified multimodal large language models (MLLMs) and multi-agent systems have advanced visual generation. However, three limitations remain. (1) Existing methods often distill task-specific experience with limited generalizability. (2) Reflection is often deferred until task completion. (3) Knowledg
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
- Rule-Compliant Visual Spatial Planning for Multimodal Large Language Models#471275
- PERSONAWEAVER: Controllable Diversity Beyond Conventional Archetypes in Procedural Character Generation#1062516
- ViFA-Council: Multi-Agent LLM Deliberation for Vietnamese Folk Art Generation#1071147
- Training and Agentic Inference Strategies for LLM-based Manim Animation Generation#798857
- Training and Agentic Inference Strategies for LLM-based Manim Animation Generation#797443
Record · ID 1110449
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