457 citations · 552 across the 18 of their papers we have counts for
6 papers · 1 filter
MemoWM: How World Models Change What Agents Need to Remember
Bingfan Zeng, Zhisheng Chen, Chenbo Sang +6
Long-term agents face growing storage demands as they accumulate experience. World models capture reusable regularities that can reduce the information stored for each experience.…
Nullify: Null-Space Activation Steering for Training-Free LLM Unlearning
Wei Zhai, Xiang Liu, Qiang Huang +6
Large Language Models (LLMs) inevitably internalize substantial amounts of sensitive or private information during pre-training, while LLM unlearning aims to selectively erase spec…
Why Are GUI Agents Correct but Late? Decode on the Decision-Time Critical Path, Tested with Pre-Compiled Policy Trees
Zihan Dong, Rui Qian, Qishi Zhan +3
Computer-use agents often fail on transient GUI events because they produce the correct action only after the relevant window has already closed. We identify the main cause as expe…
PAMNet: Cycle-aware Phase-Amplitude Modulation Network for Multivariate Time Series Forecasting
Yingbo Zhou, Yutong Ye, Zhiwei Ling +5
Reliable periodic patterns serve as a fundamental basis for accurate multivariate time series forecasting. However, existing methods either implicitly extract periodicity through c…
PAMod: Modeling Cyclical Shifts via Phase-Amplitude Modulation for Non-stationary Time Series Forecasting
Yingbo Zhou, Yutong Ye, Shuhao Li +5
Real-world time series forecasting faces the fundamental challenge of non-stationary statistical properties, including shifts in mean and variance over time. While reversible insta…
Unrewarded Exploration in Large Language Models Reveals Latent Learning from Psychology
Jian Xiong, Jingbo Zhou, Zihan Zhou +6
Latent learning, classically theorized by Tolman, shows that biological agents (e.g., rats) can acquire internal representations of their environment without rewards, enabling rapi…