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@LCM-Lab

LCM-Lab

Towards an unbounded context for foundation models
README.md

Hi there 👋

This is LCM-Lab, an open-source research team within the OpenNLG Group that focuses on long-context modeling and optimization. Below is a list of our work—please feel free to explore!

If you have any questions about the code or paper details, please don’t hesitate to open an issue or contact this email zecheng.tang@foxmail.com.

🔹 Long-Context Reward

  1. LongRM: Pushing the limits of reward modeling beyond 128K tokens GitHub arXiv

  2. MemoryRewardBench: Benchmarking Reward Models for Long-Term Memory Management in Large Language Models GitHub arXiv

🔹 Long-Context Evaluation

  1. LOOM-Eval: A comprehensive and efficient framework for long-context model evaluation GitHub arXiv

  2. L-CiteEval (ACL 2025): A faithfulness-oriented benchmark for long-context citation GitHub ACL Anthology

  3. MMLongCite: A Benchmark for Evaluating Fidelity of Long-Context Vision-Language Models GitHub arXiv

🔹 Long-Context Modeling

  1. CDT (Context Denoising Training) GitHub arXiv

  2. LOGO (ICML 2025): Long cOntext aliGnment via efficient preference Optimization GitHub ICML

  3. Global-Mamba (ACL 2025): Efficient long-context modeling architecture GitHub ACL Anthology

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  1. LOOM-Eval LOOM-Eval Public

    A comprehensive and efficient long-context model evaluation framework

    Python 28 4

  2. L-CITEEVAL L-CITEEVAL Public

    Evaluating the faithfulness of long-context language models

    Python 30 2

  3. context-denoising-training context-denoising-training Public

    context denoising training for long-context modeling

    Python 15 1

  4. LongRM LongRM Public

    Revealing and unlocking the context boundary of reward models

    Python 20

  5. LOGO LOGO Public

    Code for paper: Long cOntext aliGnment via efficient preference Optimization

    Jupyter Notebook 24

  6. MemRewardBench MemRewardBench Public

    A benchmark to systematically evaluate Reward Models' ability to assess long-term memory management of LLMs

    6 1

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