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ngocdaobao/A-Survey-on-Looped-Transformers

Language models
Total stars
105
Today
+0
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About the project

A survey of research on looped transformers — recurrent-depth architectures for language models. It organizes approaches by loop topology, loop-count policy, weight sharing and memory strategy.

Useful for
  • Find papers on a specific loop topology such as sandwich or hierarchical
  • Compare weight-sharing and training recipes for looped models before picking an architecture
  • Build a bibliography on recurrent-depth LLMs for a review or thesis

README summarized by DeepSeek V4.1 Flash. Details may be inaccurate. Read the original README →

0.0 Quiet Magnitude out of 10 — how fast interest is growing, not a quality score.

Star growth looks organic. Data as of September 25, 2026, 23:47 UTC.

The star-growth assessment does not verify whether the project is safe to run.

Stars per day

02550September 9, 2026October 29, 2026

Bars show stars per UTC day; the line is a moving average of available history. Gaps mean missing data, not zero growth. Today's bar is incomplete. Red marks spike days.

Why it’s trending

  • The repository is 16 days old and already has 105 stars.

Measured growth: the last 7 completed days

The full 7-day window is unavailable; no weekly total or comparison is calculated.

Daily measurements · UTC
October 22, 2026
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October 23, 2026
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October 24, 2026
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October 25, 2026
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October 28, 2026
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Completed days come from GitHub star history; today and forecasts are excluded from this comparison. History checked: September 20, 2026, 15:25 UTC. Sources and measurement limits →

Three GitHub discoveries in each edition

What they do, why they are gaining interest, and what to check before using them.

Editions at 10:00, 16:00, 22:00 MSK.

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Numbers

Total stars
105
Today
+0 stars on 2026-09-25 (UTC)
Forks
1
Issues and pull requests
0
Watchers
0
Created
September 9, 2026
Last push
September 10, 2026

Star trust

Growth looks organic: forks and discussion are in line with active projects, and stars arrive unevenly, the way people give them.

These are heuristics, not a verdict: we judge by the repository’s behavior, not by a list of stargazers.

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