Inspired by linguistic Olympiads, extremely low-resource language reasoning presents a unique challenge that enables models to solve problems without prior knowledge. This task mirrors the Rosetta Stone decipherment process, where the goal is to induce and apply linguistic rules from minimal context. Existing methods mainly rely on naive in-context learning that fails to handle the complexity and diversity of language rules. To mitigate this issue, we propose a framework that combines dynamic knowledge construction with task-aware evidence augmentation. First, we use large language models (LLMs) to generate a diverse set of task-specific examples that instantiate potential linguistic rules for the target low-resource language. Second, we apply a semantic retrieval mechanism to select the most relevant examples as evidence for each test query, preventing context overload and ensuring focused, analogical reasoning. Our method shifts from learning language distributions to dynamically discovering and applying rules. Experimental results on the LINGOLY and Linguini benchmark show that our approach achieves competitive performance across various LLMs, outperforming existing baselines. More importantly, our framework advances extremely low-resource reasoning and provides a generalizable framework for rule induction under knowledge constraints.

Long-context summarization is pivotal for extracting core insights from extensive documents. While Large Language Models (LLMs) show remarkable capabilities, they frequently encounter attention dilution and hallucination with lengthy inputs. Retrieval-Augmented Generation (RAG) partially mitigates this, but conventional RAG relies on shallow similarity retrieval of fragmented chunks, failing to capture high-level thematic structures and long-range dependencies. Although graph-based RAG approaches have emerged to address these structural limitations, existing solutions, such as Graph of Records (GoR), critically suffer from a fundamental flaw: they paradoxically re-introduce hallucinations by constructing graphs based on unreliable, LLM-generated responses. To overcome these challenges, we introduce Hierarchical Graph of Evidence (HiGoE) (Code link https://github.com/tkw123/HiGOE). HiGoE redefines the retrieval process by replacing unreliable chunk-based methods with a filtered proposition–evidence graph, ensuring verifiable fact grounding and substantially reducing hallucination. Moreover, HiGoE leverages Personalized PageRank (PPR) to cluster related nodes into thematic hierarchies, thereby restoring global document structure and effectively mitigating attention dilution. To model complex, multi-level relations beyond mere shallow similarity, we develop an Enhanced Graph Attention Network. Experiments show HiGoE consistently surpasses baselines in quality and efficiency.