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AllBeginnerIntermediateAdvanced
All SourcesarXiv
#compression ratio

CodeOCR: On the Effectiveness of Vision Language Models in Code Understanding

Intermediate
Yuling Shi, Chaoxiang Xie et al.Feb 2arXiv

The paper tests a simple but bold idea: show code to AI as pictures instead of plain text, then shrink those pictures to save tokens and time.

#multimodal language models#code as images#visual code understanding

SWE-Pruner: Self-Adaptive Context Pruning for Coding Agents

Intermediate
Yuhang Wang, Yuling Shi et al.Jan 23arXiv

Coding agents waste most of their tokens just reading giant files, which makes them slow and expensive.

#SWE-Pruner#context pruning#coding agents

VTCBench: Can Vision-Language Models Understand Long Context with Vision-Text Compression?

Intermediate
Hongbo Zhao, Meng Wang et al.Dec 17arXiv

Long texts are expensive for AI to read because each extra token costs a lot of compute and memory.

#vision‑text compression#VTCBench#vision‑language models