Author

Tong Mo

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#artificial intelligence Preprint Sep 2026

Highlight-Then-Summarize: Learning to Compress Evidence for Long-Context Understanding

Long-context understanding requires large language models (LLMs) to reason over lengthy documents, conversations, and code, yet task-relevant evidence is often sparse and scattered amid substantial irrelevant and redundant content. We propose Highlight-Then-Summarize (H2S), a compress-then-reason paradigm that first id...

Zhao-Yuan Xia, Qinghongbing Xie, Yung Xiang Hue et al. · 0 citations

Conference Open access 2026

MuSe: Multi-Stage Graph Reasoning via Vision-Language Models

This work proposes MuSe, a novel multi-stage graph reasoning framework based on VLMs, where instead of processing entire graphs at once, MuSe incrementally samples and visualizes task-relevant subgraphs, enabling progressive reasoning.

Guanyu Wang, Xu Chu, Zhijie Tan et al. · Annual Meeting of the Associ... · 1 citation