1 citations · 1 across the 6 of their papers we have counts for
6 papers
Kintsugi: Learning Policies by Repairing Executable Knowledge Bases
Teng Cao, Yu Deng, Hikaru Shindo +6
Modern embodied agents achieve impressive performance, but their task knowledge is often stored in neural weights, latent state, or prompt-bound memory, making individual policy kn…
Does RAG Know When Retrieval Is Wrong? Diagnosing Context Compliance under Knowledge Conflict
Yihang Chen, Pin Qian, Su Wang +4
Retrieval-Augmented Generation (RAG) is usually evaluated by whether the final answer is correct. Under knowledge conflict, this hides a key question: did the model follow retrieve…
CogniFold: Always-On Proactive Memory via Cognitive Folding
Suli Wang, Yiqun Duan, Yu Deng +6
Existing agent memory remains predominantly reactive and retrieval-based, lacking the capacity to autonomously organize experience into persistent cognitive structure. Toward genui…
ECHO-2: A Large-Scale Distributed Rollout Framework for Cost-Efficient Reinforcement Learning
Jingwei Song, Meng Chen, Jie Xiao +15
Reinforcement learning (RL) is a critical stage in post-training large language models (LLMs), involving repeated interaction between rollout generation, reward evaluation, and cen…
Lattica: A Decentralized Cross-NAT Communication Framework for Scalable AI Inference and Training
Ween Yang, Jason Liu, Suli Wang +4
The rapid expansion of distributed Artificial Intelligence (AI) workloads beyond centralized data centers creates a demand for new communication substrates. These substrates must o…
NeuroTTT: Bridging Pretraining-Downstream Task Misalignment in EEG Foundation Models via Test-Time Training
Suli Wang, Yangshen Deng, Zhenghua Bao +2
Large-scale foundation models for EEG signals offer a promising path to generalizable brain-computer interface (BCI) applications, but they often suffer from misalignment between p…