arXiv (All)
open access
PolyGandr: Steering Generative Models via Spatial Navigation through Latent Space
Generative models support content creation across text, images, videos, and design disciplines, yet their interfaces rely primarily on natural language. While effective for specifying high-level intent, language can be inefficient and imprecise for fine-grained control, iterative refinement, and ope
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
- NaviScale: Generating Large-Scale Semantic Map Datasets for Object Navigation#1063429
- OSPO: Object-Centric Self-Improving Preference Optimization for Text-to-Image Generation#1105911
- ImagiNav: Scalable Embodied Navigation via Generative Visual Prediction and Inverse Dynamics#1249361
- NavHarness: Towards Lifelong Embodied Navigation#1119349
- Inside-JEPA: From Prediction to Relational Grounding in Latent World Models#1424312
Record · ID 1469049
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