Efficient Context Scaling with LongCat ZigZag Attention

We introduce LongCat ZigZag Attention (LoZA), which is a sparse attention scheme designed to transform any existing full-attention models into sparse versions with rather limited compute budget. In long-context scenarios, LoZA can achieve significant speed-ups both for prefill-intensive (e.g., retrieval-augmented generation) and decode-intensive (e.g., tool-integrated reasoning) cases. Specifically, by applying LoZA to LongCat-Flash during mid-training, we serve LongCat-Flash-Exp as a long-context foundation model that can swiftly process up to 1 million tokens, enabling efficient long-term reasoning and long-horizon agentic capabilities.

Data Discovery for the SDGs: A Systematic Rule-based Approach

In 2015, the United Nations put forward 17 Sustainable Development Goals (SDGs) to be achieved by 2030, where data has been promoted as a focus to innovating sustainable development and as a means to measuring progress towards achieving the SDGs. In this study, we propose a systematic approach towards discovering data types and sources that can be used for SDG research. The proposed method integrates a systematic mapping approach using manual qualitative coding over a corpus of SDG-related research literature followed by an automated process that applies rules to perform data entity extraction computationally. This approach is exemplified by an analysis of literature relating to SDG 7, the results of which are also presented in this paper. The paper concludes with a discussion of the approach and suggests future work to extend the method with more advance NLP and machine learning techniques.