CNY让LLM自主决定何时扩展邻居获取证据，解决了传统方法静态上下文的局限，在TAG推理任务上表现更好。
Call Neighbours Yourself (CNY)是一种新型图推理框架，使LLM能够通过拓扑约束的图行走动作主动探索图邻域。CNY在标准TAG推理基准测试中表现优于固定上下文基线，其探索策略可迁移到未见过的图和图级任务。该研究引入了目标条件在线自蒸馏技术，解决了邻域探索的延迟信用挑战。代码已在GitHub开源。
Call Neighbours Yourself: Graph Walks with Destination-Conditioned On-Policy Self-Distillation
Reasoning over text-attributed graphs (TAGs) requires large language models (LLMs) to combine a node's text with evidence distributed across its neighbourhood. Existing methods fix the set of accessible neighbours before generation, forcing reasoning to operate over a static context and preventing the model from acquiring missing evidence during inference. We argue that neighbour selection should itself be part of the reasoning process. To this end, we propose Call Neighbours Yourself (CNY), a framework that enables LLMs to proactively explore graph neighbourhoods through topology-constrained graph-walk actions. Instead of reasoning over a pre-selected neighbour set, CNY exposes lightweight neighbour previews and learns when to expand candidate neighbours for additional evidence. To address the delayed-credit challenge of neighbour exploration, we introduce destination-conditioned on-policy self-distillation, which retrospectively evaluates a selected neighbour after its content is revealed and converts the resulting change in action preference into an action-level training signal. Experiments on standard TAG reasoning benchmarks under a unified raw-text setting show that CNY consistently outperforms fixed-context post-training baselines. Furthermore, the learned exploration policy transfers to unseen graphs and to a graph-level task not encountered during training. Code is available at https://github.com/superallen13/CNY.