arXiv (All)
open access
Repurposing Pre-trained LLMs as High Fidelity Continuous Text Autoencoders
Next-token prediction has enabled highly fluent autoregressive language models, but it represents global structure only indirectly through sequential factorization. In contrast, high-fidelity autoencoders have become a standard primitive in image generation, enabling generative models to operate ove
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
- Embedding Prediction Helps Image Generation#1161011
- Embedding Prediction Helps Image Generation#1157551
- Official Review · NEURIPS 2023#787218
- TPSO: Training-Free Diverse Image Generation via Semantic Prompt Embedding Optimization#799913
- CausalEmbed: Auto-Regressive Multi-Vector Generation in Latent Space for Visual Document Embedding#1061671
Record · ID 1063459
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