4 papers
CRE-T1 Preview Technical Report: Beyond Contrastive Learning for Reasoning-Intensive Retrieval
Guangzhi Wang, Yinghao Jiao, Zhi Liu
The central challenge of reasoning-intensive retrieval lies in identifying implicitreasoning relationships between queries and documents, rather than superficial se-mantic or lexic…
Refine Thought: A Test-Time Inference Method for Embedding Model Reasoning
Guangzhi Wang, Kai Li, Yinghao Jiao +1
We propose RT (Refine Thought), a method that can enhance the semantic reasoning ability of text embedding models. The method obtains the final semantic representation by running m…
Automated Prompt Generation for Creative and Counterfactual Text-to-image Synthesis
Aleksa Jelaca, Ying Jiao, Chang Tian +1
Text-to-image generation has advanced rapidly with large-scale multimodal training, yet fine-grained controllability remains a critical challenge. Counterfactual controllability, d…
Structured Information for Improving Spatial Relationships in Text-to-Image Generation
Sander Schildermans, Chang Tian, Ying Jiao +1
Text-to-image (T2I) generation has advanced rapidly, yet faithfully capturing spatial relationships described in natural language prompts remains a major challenge. Prior efforts h…