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I've been working on ctoth/research-papers-plugin, the pipeline to actually get LLMs to extract the notes. I really like your insight re RST over Markdown! It sounds like we're working on similar stuff and I'll absolutely reach out :)
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I'm gonna look at your plugin. My email is in my profile.

Honestly I think that Markdown with LateX code blocks would be the most efficient representation but when doing it with Pandoc I kept having issues with loss of information and sometimes even syntax error.

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This sounds like it would work, but honestly if you've already read all 30 papers fully, what do you still need to llm to do for you? Just the boilerplate?
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I'm trying to make a go library that implements a wide ranges of MOT algorithms and can gather metrics for all of them.

Reading all the papers once isn't the same as this. I find it very useful.

I can ask an LLM to do the basic implementations, then I can refine them (make the code better, faster, cut on memory use), then I can ask the LLM if I'm still implementing the algorithms as they're described in the paper.

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sounds similar to "LLM Knowledge Bases" https://xcancel.com/karpathy/status/2039805659525644595
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Does that even fit in the context? It seems like 30 papers worth of content would just overflow it.
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For each paper, have your agent extract a three sentence description, create a description.md, then concat those with the paper names into an INDEX.md which it should consult to find appropriate papers. Also: have your agent tag papers, then autogenerate your tagged collection on the filesystem. Then you get nice things like https://github.com/ctoth/Qlatt/tree/master/papers/tagged

Then something in your {CLAUDE,AGENTS}.md that says: when working on something with relevant context supplied by papers, read the papers before doing the work. You can find all papers plus their descriptions in ./papers/INDEX.md and papers by tag in ./papers/tagged

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