14 papers
AI Research Agents Narrow Scientific Exploration
Yixuan Tang, Yi Yang
AI research agents now support large-scale AI-assisted scientific discovery. We examine whether AI-generated ideas broaden scientific exploration or primarily reinforce existing wo…
DREAM: Dense Retrieval Embeddings via Autoregressive Modeling
Yixuan Tang, Yi Yang
Dense retrieval embedding models are a fundamental component of modern retrieval-based AI systems. Most dense retrievers are trained with contrastive objectives, which require labe…
FLARE: Task-agnostic embedding model evaluation through a normalization process
Jingzhou Jiang, Yixuan Tang, Yi Yang +1
When task-specific labels are not available, it becomes difficult to select an embedding model for a specific target corpus. Existing labelless measures based on kernel estimators…
Crowded in B-Space: Calibrating Shared Directions for LoRA Merging
Yixuan Tang, Yi Yang
Merging separately trained LoRA adapters is a practical alternative to joint multi-task training, but it often hurts performance. Existing methods usually treat the LoRA update $Î…
Mind the Shift: Decoding Monetary Policy Stance from FOMC Statements with Large Language Models
Yixuan Tang, Yi Yang
Federal Open Market Committee (FOMC) statements are a major source of monetary-policy information, and even subtle changes in their wording can move global financial markets. A cen…
KV-Embedding: Training-free Text Embedding via Internal KV Re-routing in Decoder-only LLMs
Yixuan Tang, Yi Yang
While LLMs are powerful embedding backbones, their application in training-free settings faces two structural challenges: causal attention restricts early tokens from accessing sub…