4 papers
BEAR: Budgeted Evidence Allocation for Multi-Document Reasoning
Lin Sun, Linglin Zhang, Jingang Huang +3
We argue that multi-document reasoning is constrained not only by how much text a model can read, but also by how limited query-time evidence budget is allocated across documents a…
When Good OCR Is Not Enough: Benchmarking OCR Robustness for Retrieval-Augmented Generation
Lin Sun, Wang Dexian, Jingang Huang +4
Industrial Retrieval-Augmented Generation (RAG) systems depend on optical character recognition (OCR) to transform visual documents into text. Existing OCR benchmarks rely on chara…
TinyR1-32B-Preview: Boosting Accuracy with Branch-Merge Distillation
Lin Sun, Guangxiang Zhao, Xiaoqi Jian +18
The challenge of reducing the size of Large Language Models (LLMs) while maintaining their performance has gained significant attention. However, existing methods, such as model di…
Evaluation is All You Need: Strategic Overclaiming of LLM Reasoning Capabilities Through Evaluation Design
Lin Sun, Weihong Lin, Jinzhu Wu +8
Reasoning models represented by the Deepseek-R1-Distill series have been widely adopted by the open-source community due to their strong performance in mathematics, science, progra…