Solving long-range recall in linear attention for DNA sequences
No-Coffee-8227 · reddit · 2026-08-16
- Issue: When using linear attention for DNA sequences up to 1M tokens, long-range recall drops to 25% (near-random chance) in Needle-in-a-Haystack tests.
- Observations: Existing models like HyenaDNA show similar poor performance, while smaller models perform well (50-60%) at 16K context, suggesting a length-related issue.
- Limitations: Current solutions often rely on external memory, sliding windows, or hybrid architectures, which the author wants to avoid.
- Question: Is this a fundamental limitation of linear attention's compressed state, or are there architectural approaches that can preserve retrieval?
Related event: Linear Attention Struggles with Recall in Long DNA Sequences(2 posts)→
More from Research
- TSUI: Native UI Framework Compiling TS/XML to GPU — johnlindquist · 2026-08-24
- Anthropic Study: Fine-Tuned Lie Detectors Fail to Generalize OOD — PandaAshwinee · 2026-08-24
- Shengshu Tech Unveils 5-Stage Roadmap for General World Models — 生数科技 · 2026-08-24
- AGI May Arrive First in Hard Tech Due to Objective Feedback Loops — imjustnewatai · 2026-08-24
- Trained a 1.57B-parameter Dreamer 4 World Model from scratch for under $150 — OtherRaisin3426 · 2026-08-24
- Graph Engineering organizes multi-agent systems via dynamic structures — Yuyuan Feng · 2026-08-24