LLM Knowledge Bases Something I'm finding very useful recently: using LLMs to build personal knowledge bases for various topics of research interest. In this way, a large fraction of my recent token throughput is going less into manipulating code, and more into manipulating knowledge (stored as markdown and images). The latest LLMs are quite good at it. So: Data ingest: I index source documents (articles, papers, repos, datasets, images, etc.) into a raw/ directory, then I use an LLM to incrementally "compile" a wiki, which is just a collection of .md files in a directory structure. The wiki includes summaries of all the data in raw/, backlinks, and then it categorizes data into concepts, writes articles for them, and links them all. To convert web articles into .md files I like to use the Obsidian Web Clipper extension, and then I also use a hotkey to download all the related images to local so that my LLM can easily reference them. IDE: I use Obsidian as the IDE "frontend" where I can view the raw data, the the compiled wiki, and the derived visualizations. Important to note that the LLM writes and maintains all of the data of the wiki, I rarely touch it directly. I've played with a few Obsidian plugins to render and view data in other ways (e.g. Marp for slides). Q&A: Where things get interesting is that once your wiki is big enough (e.g. mine on some recent research is ~100 articles and ~400K words), you can ask your LLM agent all kinds of complex questions against the wiki, and it will go off, research the answers, etc. I thought I had to reach for fancy RAG, but the LLM has been pretty good about auto-maintaining index files and brief summaries of all the documents and it reads all the important related data fairly easily at this ~small scale. Output: Instead of getting answers in text/terminal, I like to have it render markdown files for me, or slide shows (Marp format), or matplotlib images, all of which I then view again in Obsidian. You can imagine many other visual output formats depending on the query. Often, I end up "filing" the outputs back into the wiki to enhance it for further queries. So my own explorations and queries always "add up" in the knowledge base. Linting: I've run some LLM "health checks" over the wiki to e.g. find inconsistent data, impute missing data (with web searchers), find interesting connections for new article candidates, etc., to incrementally clean up the wiki and enhance its overall data integrity. The LLMs are quite good at suggesting further questions to ask and look into. Extra tools: I find myself developing additional tools to process the data, e.g. I vibe coded a small and naive search engine over the wiki, which I both use directly (in a web ui), but more often I want to hand it off to an LLM via CLI as a tool for larger queries. Further explorations: As the repo grows, the natural desire is to also think about synthetic data generation + finetuning to have your LLM "know" the data in its weights instead of just context windows. TLDR: raw data from a given number of sources is collected, then compiled by an LLM into a .md wiki, then operated on by various CLIs by the LLM to do Q&A and to incrementally enhance the wiki, and all of it viewable in Obsidian. You rarely ever write or edit the wiki manually, it's the domain of the LLM. I think there is room here for an incredible new product instead of a hacky collection of scripts.

🔎 LLM agents rewrite a decompiler's unreadable `local_48`-laden code to be readable while preserving function, but a single metric collapses into "gaming." The fix is a multidimensional readability score. Title: LLM Agent-Assisted Reverse Engineering with Quantitative Readability Metrics URL: 📝 Overview This paper has LLM agents improve the readability of decompiled binaries while keeping functional correctness. The key is QRS, a multidimensional score combining structural validation with three readability sub-metrics. ❓ Challenges Solved Automated decompilers produce functionally correct but unreadable code. When LLMs try to fix it, without quantitative guidance they lose focus, and optimizing a single metric leads to "gaming" that sacrifices other dimensions. 💡 Methodology & Proposed Approach ・QRS is a structural gate times a composite score, a weighted sum of lexical surprisal, structural simplicity, and idiomatic quality ・Lexical surprisal uses a small code-LLM's perplexity to measure how familiar the code looks ・Structural simplicity uses cyclomatic complexity and nesting depth; idiomatic quality uses clang-tidy anti-pattern checks ・QRS is computed only if the recompiled code reaches at least 0.85 CFG similarity to the original binary in radare2 🎯 Use Cases It directly speeds up reading decompiler output in malware analysis, vulnerability research, legacy-software comprehension, and patch diffing. 📊 Experimental Results ・On 210 synthetic C binaries, LLM-only reached QRS at least 0.75 in 74.76% of cases, with QRS up +0.420 on average and zero regressions ・Allowing Bash execution raised the rate to 82%, improved QRS by +0.509, and cut iterations from 5.92 to 2.933 (a 43% reduction) ・It empirically shows that going multidimensional avoids Goodhart's Law, "when a measure becomes a target, it stops being a good measure" #ReverseEngineering# #AIAgents#