andthattoo

andthattoo / structured-cot

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Structured Chain-of-Thought

49
2
100% credibility
Found Apr 26, 2026 at 49 stars -- GitGems finds repos before they trend. Get early access to the next one.
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AI Analysis
Python
AI Summary

A demonstration project that tests and visualizes how forcing AI models to use structured reasoning formats drastically cuts down on verbose thinking tokens while maintaining high accuracy on coding benchmarks.

How It Works

1
🔍 Discover the trick

You stumble upon a clever idea that makes AI think much shorter and smarter when solving coding puzzles, without losing any brainpower.

2
📥 Get it ready

You grab the simple tools needed to try it out on your computer, following easy steps like preparing a workspace.

3
🐘 Add the AI brain

You download a powerful AI model that knows how to code, saving it to a folder on your machine.

4
🚀 Wake up the AI

With one command, you start your local AI helper, ready to tackle coding challenges efficiently.

5
⚖️ Compare thinking styles

You run tests on real coding problems, pitting free-flowing thoughts against super-compact structured plans.

6
📊 Review the magic

You check out charts, summaries, and fun animations showing how structured thinking crushes verbose rambling.

🎉 Unlock efficiency

You see firsthand how AI can solve the same problems using 22 times fewer thinking steps, opening doors to faster, cheaper smarts.

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Star Growth

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AI-Generated Review

What is structured-cot?

Structured-cot delivers grammar-constrained chain-of-thought prompting for LLMs, forcing compact reasoning like GOAL/APPROACH/EDGE inside tags via GBNF on llama.cpp servers. This Python tool tackles verbose CoT in code generation, compressing thinking tokens 22x on HumanEval+ and boosting pass@1 by 14pp on recent LiveCodeBench problems—no training required. Users get eval scripts to benchmark free-form vs structured modes on datasets like MBPP+ or LeetCode slices.

Why is it gaining traction?

It proves structured chain-of-thought prompting for code generation works out-of-box, matching full CoT accuracy at a fraction of tokens via inference-time grammars. Devs dig the side-by-side animations and JSONL outputs showing extraction reliability, plus easy local servers for Qwen models. Unlike openai structured output, it's github llm structured output you host yourself, dodging API costs.

Who should use this?

Inference engineers serving code-gen LLMs at scale, AI researchers testing structured chain of thought limits on math/logic tasks, teams evaluating github structured cot on custom benchmarks like LiveCodeBench LeetCode.

Verdict

Worth testing for token-hungry reasoning pipelines—benchmarks and server scripts make it plug-and-play. With 49 stars and 1.0% credibility score, it's raw but well-documented; mature it with cross-model runs before prod.

(198 words)

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