Linear attention

Evidence in the last complete seven days

Source mentions

1

Previous seven days 2

Distinct sources

1

Previous 2

Comparable growth

Not comparable

Ranking appearances

0

Collection observations and undated records are separate from dated publication counts. Observed in 7d：0 · Undated records：0

Daily history · up to 90 days

Bars show dated source mentions. The latest UTC day may be incomplete. Zero mentions and missing collection coverage are different conditions.

Dotted markers indicate days with no record. They are not counted as zero mentions.

Source evidence

Sources in the evidence shown：HF Daily Papers 1 · GitHub Issues 2 · arXiv 1

1. HF Daily Papers · Published Nov 2, 2026

   SpecOPD: Branch-Parallel On-Policy Distillation for Speculative Decoding

   Speculative decoding (SD) accelerates large language model inference by reducing the sequential cost of token-by-token generation. Advanced SD methods reduce proposal cost with parallel drafters that predict entire blocks in one forward pass, but overall speedup still depends on acceptance length. On-policy distillation (OPD) can push well-trained drafters b

2. GitHub Issues · Published Oct 28, 2026

   [hardware support] Moore Threads GPU (MUSA) support in flash linear attention

   View source record →

3. arXiv · Published Oct 22, 2026

   Building AI Research Teams for Autonomous Discovery of Linear Attention Architectures

   arXiv:2610.28887v1 Announce Type: new Abstract: Autonomous agents can increasingly propose ideas, modify code, and run experiments, creating an opportunity to accelerate scientific discovery using teams of collaborative AI researchers. To escape local minima in the discovery, we propose to encourage orthogonal exploration by constructing the team using agent

   View source record →

4. GitHub Issues · Published Oct 9, 2026

   Torch fallbacks of the linear-attention ops drop `seq_idx` / `cu_seqlens`, so packed documents leak without the optional packages

   View source record →

Review priority 41 / 100

This score orders research candidates. It is not search volume, a forecast or verified commercial demand.

• Evidence

  8 / 30

  Distinct dated articles or first observations; repeat crawling adds nothing.

• Publisher diversity

  8 / 25

  Conservative owner grouping; syndicated copies count once.

• Comparable change

  0 / 20

  Comparable complete weeks only; no percentage from a zero baseline.

• Recent evidence

  15 / 15

  Latest publication or first observation, not repeated fetch time.

• Name specificity

  9.7 / 10

  Explicit entities and technical concepts outrank ambiguous acronyms.

Limitations and interpretation

• First observation is a collection milestone, not proof that a word or product is new to the world.
• Counts describe monitored public sources, not the whole web or search demand.
• First observed here is not the invention date of a name or idea.
• Candidates come from public titles, summaries and entity URLs, not verified full-text analysis.
• Owner grouping is conservative; undisclosed syndication may remain.
• Repeat ranking appearances are observations, not new articles or independent endorsements.
• An established concept or common product name retained as a baseline, not a newly invented term.
• Both weeks need adequate, comparable collection coverage.
• Sources behind this term changed or lack a comparable baseline.
• Recent evidence includes collection over three days after publication.
• Current evidence comes from one publisher or owner.