A proposal suggests using semantic compression as an input diffusion technique to handle AI sessions longer than the current context window. This method treats the context like a progressive render, starting with a compressed outline and gradually adding less compressed, more detailed slices. The goal is to preserve non-local information that is lost in standard compaction or retrieval methods. Initial tests with small, untrained models like Qwen2.5 7B show potential for individual components but struggle with end-to-end coherence, with further fine-tuning planned to assess position-aware training. AI
IMPACT Could enable AI models to maintain coherence and recall information across much longer interactions.
RANK_REASON Research proposal for a novel technique in handling long AI contexts. [lever_c_demoted from research: ic=1 ai=1.0]
- context window
- Input Diffusion and the Evolution of Production Networks
- Qwen2.5 7B
- Recursive Language Models
- Semantic compression
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