Autoregressive models are trained to predict a system's behavior one step at a time, and recursive generation allows the learned dynamics to unfold over long horizons. To what extent can such dynamics learned from local observations recover broader organization of an underlying system that was only partially observed d...
Yi-Lun Liu, Yi Zhang, Gan-Yu Wu et al.· 0 citations
Agent skills, reusable procedural documents that extend LLM agents beyond their parametric memory, have become an important interface for deploying agents on real-world tasks. Community-maintained skill libraries built around this interface are growing rapidly. However, this ecosystem remains deeply English-centric: ou...
Yi-Lun Liu, Shi-Min Tao, Ming-Gui He et al.· 0 citations
This work proposes Call Neighbours Yourself (CNY), a framework that enables LLMs to proactively explore graph neighbourhoods through topology-constrained graph-walk actions and introduces destination-conditioned on-policy self-distillation, which retrospectively evaluates a selected neighbour after its content is revea...
Yilun Liu, Bo-Yu Luo, Yanran Tang et al.· 0 citations
According to Mendelian principles of controlled inheritance, Mendel G\"odel Machine (MGM) is introduced, which includes two new types of self-modification that better utilizes evidences accumulated and facilitates a faster and better convergence over single-trajectory baselines.
Changzhi Liu, Yilun Liu, Sikuan Yan et al.· 0 citations
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