10 papers · 1 filter
Distilling Text LMs into Speech-to-Speech LMs by Matching Internal Text Predictions
Ju-Chieh Chou, Jiawei Zhou, Karen Livescu
Text language models (LMs) acquire broad linguistic and factual capabilities through large-scale pretraining, while comparable capabilities remain difficult to learn from speech al…
Reduced Matrix Multiplication: Input-Adaptive Matrix-Product Reduction for LLM Inference
Zixuan Lan, Yanhong Li, Jiawei Zhou
Transformer-based language models achieve strong performance but incur substantial inference cost due to repeated high-dimensional matrix multiplications. We propose Reduced Matrix…
VISTA: An End-to-End Benchmark for Visual Spec-to-Web-App Coding Agents
JunJia Guo, Yuhang Yao, Jiawei +4
Coding agents can now build applications from design mockups, but a screen that looks right is not an application that works. Existing benchmarks either score visual reconstruction…
Looped World Models
Hongyuan Adam Lu, Z. L. Victor Wei, Qun Zhang +28
Current world models face a fundamental tension: faithful long-horizon simulation demands deep computation, but deeper models are expensive to deploy and prone to compounding error…
Not All Skills Help: Measuring and Repairing Agent Knowledge
Yixuan Wang, Yiyang Zhou, Yiming Liang +4
LLM agents can improve without weight updates by accumulating natural-language skills from experience, but current systems entrust every decision about which skills to keep and how…
AutoResearchClaw: Self-Reinforcing Autonomous Research with Human-AI Collaboration
Jiaqi Liu, Shi Qiu, Mairui Li +33
Automating scientific discovery requires more than generating papers from ideas. Real research is iterative: hypotheses are challenged from multiple perspectives, experiments fail…
Seeing without Looking: Do Vision-Language Benchmarks Really Test Vision?
Zixuan Lan, Luzhe Sun, Matthew R. Walter +1
Benchmark accuracy is often implicitly assumed to reflect grounded visual understanding in vision-language models (VLMs), yet it remains unclear to what extent such scores truly re…
DocScope: Benchmarking Verifiable Reasoning for Trustworthy Long-Document Understanding
Xiang Feng, Jiawei Zhou, Zhangfeng Huang +6
Evaluating whether Multimodal Large Language Models can produce trustworthy, verifiable reasoning over long, visually rich documents requires evaluation beyond end-to-end answer ac…
ORCE: Order-Aware Alignment of Verbalized Confidence in Large Language Models
Chen Li, Xiaoling Hu, Songzhu Zheng +2
Large language models (LLMs) often produce answers with high certainty even when they are incorrect, making reliable confidence estimation essential for deployment in real-world sc…
PRISM: A Dual View of LLM Reasoning through Semantic Flow and Latent Computation
Ruidi Chang, Jiawei Zhou, Hanjie Chen
Large language models (LLMs) solve complex problems by generating multi-step reasoning traces. Yet these traces are typically analyzed from only one of two perspectives: the sequen…