11 papers
Why Sample What You Can Enumerate? Exact Policy Optimization for Genomic Tool Selection
Haoyue Liu, Xiaoyu Ma, Ye Chen +2
Reinforcement learning over a frozen reasoner has become a common recipe for teaching a policy which external tools to invoke. We show that this recipe becomes structurally mismatc…
SEPO: Evidence-Grounded Prompt Optimization via Structural Editing
Xiaoyu Ma, Haoyue Liu, Yiwen Li +4
Existing API-only prompt optimisers are often described as interpretable, but in practice, this usually means only post-hoc inspectability: each iteration still rewrites the prompt…
Do SpeechLMs Hear Their Own Opinions? Diagnosing and Mitigating Previous-Belief Contamination in Streaming Emotion Understanding
Haoyue Liu, Zhichao Wang, Ye Chen +2
Streaming emotion understanding uses historical state while continuously interpreting current audio, often feeding the model's previous prediction back as context. We show that thi…
Which Negatives Matter? Ask Your Text Encoder: Adaptive Similarity Margins for Dense-Caption Retrieval
Haoyue Liu, Ye Chen, Zhichao Wang +1
Dense-caption retrieval has recently been improved by introducing segmentation, edge maps, LLM-filtered captions, and cross-modal modules into contrastive fine-tuning. However, the…
Are Prompt Optimizers Blind? Cross-Modal Visual Feedback for Automatic Prompt Optimization
Haoyue Liu, Xiaoyu Ma, Ye Chen +2
Automatic prompt optimization (APO) has been widely adopted to adapt vision-language models (VLMs) to downstream tasks without weight updates, yielding promising results. However,…
One Rewrite to Fix Them All? Type-Aware Repair Allocation for Text-to-Image Prompt Optimization
Haoyue Liu, Xiaoyu Ma, Ye Chen +2
Text-to-image (T2I) generators often fail to follow their prompts faithfully, producing wrong counts, swapped attributes, ambiguous relations, and illegible text. Prompt optimizati…