2 citations · 2 across the 9 of their papers we have counts for
9 papers
Escaping the Context Bottleneck: Active Context Curation for LLM Agents via Reinforcement Learning
Xiaozhe Li, Tianyi Lyu, Yizhao Yang +6
Large Language Models (LLMs) struggle with long-horizon tasks due to the "context bottleneck" and the "lost-in-the-middle" phenomenon, where accumulated noise from verbose environm…
COINBench: Moving Beyond Individual Perspectives to Collective Intent Understanding
Xiaozhe Li, Tianyi Lyu, Siyi Yang +6
Understanding human intent is a high-level cognitive challenge for Large Language Models (LLMs), requiring sophisticated reasoning over noisy, conflicting, and non-linear discourse…
Fine-tuning Small Language Models as Efficient Enterprise Search Relevance Labelers
Yue Kang, Zhuoyi Huang, Benji Schussheim +19
In enterprise search, building high-quality datasets at scale remains a central challenge due to the difficulty of acquiring labeled data. To resolve this challenge, we propose an…
ConsintBench: Evaluating Language Models on Real-World Consumer Intent Understanding
Xiaozhe Li, TianYi Lyu, Siyi Yang +6
Understanding human intent is a complex, high-level task for large language models (LLMs), requiring analytical reasoning, contextual interpretation, dynamic information aggregatio…
High-Fidelity Synthetic ECG Generation via Mel-Spectrogram Informed Diffusion Training
Zhuoyi Huang, Nutan Sahoo, Anamika Kumari +13
The development of machine learning for cardiac care is severely hampered by privacy restrictions on sharing real patient electrocardiogram (ECG) data. Although generative AI offer…
TrInk: Ink Generation with Transformer Network
Zezhong Jin, Shubhang Desai, Xu Chen +8
In this paper, we propose TrInk, a Transformer-based model for ink generation, which effectively captures global dependencies. To better facilitate the alignment between the input…