Long-context reasoning remains a critical challenge for Large Language Models (LLMs). Current research typically focuses on curating specialized training data by treating long-context reasoning as a monolithic task, yet it is challenging to verify its quality due to its internal cognitive complexity. In this paper, we propose a paradigm shift: viewing long-context reasoning as a hierarchical spectrum of core atomic skills. To operationalize this, we present an Anchor-based Reasoning (AbR) pipeline that automatically synthesizes strictly verifiable training data for each skill, thereby enabling reliable quality. Diagnostic analyses confirm that proficiency in these atomic skills strongly predicts general long-context reasoning performance. By training exclusively on a highly compact set of merely four thousand synthesized samples with Reinforcement Learning (RL), we explicitly sharpen these foundational capabilities. Extensive experiments across six challenging benchmarks demonstrate that this targeted RL causally delivers robust and generalizable performance gains, outperforming DeepSeek-R1-distill-32B by an average margin of 7.7% (improving from 46.3% to 54.0%). Furthermore, in-depth ablations reveal that these skills operate synergistically, establishing a scalable, data-efficient, and verifiable path to advancing long-context reasoning.

Linguistic steganography (stego) seeks to conceal secret information within natural language text. However, existing methods often struggle to balance stego text quality with embedding efficiency, largely due to limitations in generation strategies and coding mechanisms. We propose SA-ANS, a self-adaptive linguistic steganography framework based on a self-adjusting Asymmetric Numeral System. SA-ANS allows user-specified embedding rates and uses probabilistic coding with adaptive candidate selection, dynamically tailoring the token pool to the language model’s probability distribution. This design produces fluent, semantically coherent stego text while preserving statistical indistinguishability from natural language. Extensive experiments on multiple benchmark datasets, evaluated across embedding efficiency, linguistic quality, statistical similarity, robustness to steganalysis, and human judgment, show that SA-ANS consistently outperforms state-of-the-art methods, demonstrating both effectiveness and practicality.

Commonsense knowledge has been widely considered for building intelligent open-domain dialogue agents, aiming to generate meaningful and diverse responses. Previous works in this field usually lack the ability to effectively obtain and utilize auxiliary commonsense from the external visual world. In this paper, we argue that exploiting logical information in images related to context can be effective to enrich and steer the generation process. In view of this, we propose VICTOR, a context-relevant VIsual Commonsense enhanced dialogue generaTOR for generating coherent and informative responses. To obtain the associated visual commonsense, we devise a novel approach that expands topic words on the knowledge graph and maps them into daily scenarios. During the generation, the model adopts multimodal fusion mechanism to integrate visual and textual information, and adaptively combine their decoding distributions for better response generation. The experimental results on two public datasets show that our proposed method outperforms the latest competitive methods in terms of coherence and diversity.