2 citations · 3 across the 15 of their papers we have counts for
8 papers · 1 filter
Measuring Aleatoric and Epistemic Uncertainty in LLMs: Empirical Evaluation on ID and OOD QA Tasks
Kevin Wang, Subre Abdoul Moktar, Jia Li +2
Large Language Models (LLMs) have become increasingly pervasive, finding applications across many industries and disciplines. Ensuring the trustworthiness of LLM outputs is paramou…
Xihe: Scalable Zero-Shot Time Series Learner Via Hierarchical Interleaved Block Attention
Yinbo Sun, Yuchen Fang, Zhibo Zhu +7
The rapid advancement of time series foundation models (TSFMs) has been propelled by migrating architectures from language models. While existing TSFMs demonstrate impressive perfo…
T3 Planner: A Self-Correcting LLM Framework for Robotic Motion Planning with Temporal Logic
Jia Li, Guoxiang Zhao
Translating natural language instructions into executable motion plans is a fundamental challenge in robotics. Traditional approaches are typically constrained by their reliance on…
Conda: Column-Normalized Adam for Training Large Language Models Faster
Junjie Wang, Pan Zhou, Yiming Dong +6
Large language models (LLMs) have demonstrated impressive generalization and emergent capabilities, yet their pre-training remains computationally expensive and sensitive to optimi…
FeatBench: Towards More Realistic Evaluation of Feature-level Code Generation
Haorui Chen, Chengze Li, Jia Li
Evaluating Large Language Models (LLMs) on repository-level feature implementation is a critical frontier in software engineering. However, establishing a benchmark that faithfully…
Agent4S: The Transformation of Research Paradigms from the Perspective of Large Language Models
Boyuan Zheng, Zerui Fang, Zhe Xu +13
While AI for Science (AI4S) serves as an analytical tool in the current research paradigm, it doesn't solve its core inefficiency. We propose "Agent for Science" (Agent4S)-the use…
SR: Teaching LLMs to Self-verify and Self-correct via Reinforcement Learning
Ruotian Ma, Peisong Wang, Cheng Liu +6
Recent studies have demonstrated the effectiveness of LLM test-time scaling. However, existing approaches to incentivize LLMs' deep thinking abilities generally require large-scale…
Goedel-Prover: A Frontier Model for Open-Source Automated Theorem Proving
Yong Lin, Shange Tang, Bohan Lyu +8
We introduce Goedel-Prover, an open-source language model that achieves state-of-the-art (as of April 5 2025) performance in automated formal proof generation for mathematical prob…