badaramoni

badaramoni / wave-field-llm

Public

An O(n log n) language model architecture using wave equation dynamics instead of O(n²) self-attention. Within 5% of standard transformer quality.

433
60
100% credibility
Found Feb 20, 2026 at 31 stars 14x -- GitGems finds repos before they trend. Get early access to the next one.
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AI Analysis
AI Summary

An open-source research project implementing a novel language model architecture that simulates wave propagation in physical fields for efficient text processing.

How It Works

1
🔍 Discover the project

You hear about a clever new way to build AI that mimics how waves spread information in physics, promising smarter and faster language understanding.

2
📖 Explore the idea

You read simple explanations and results showing it matches top AI performance but uses much less computing power for long texts.

3
🚀 See the magic

Benchmarks reveal huge speed gains—like 100 times faster on long stories—while keeping quality close to the best models.

4
💻 Set it up

Download the ready files and prepare your computer with everyday tools to start experimenting.

5
📚 Teach it language

Give it example texts like Wikipedia articles so it learns to predict and understand words naturally.

6
🧪 Check its smarts

Run quick tests to measure how well it handles puzzles and generates sensible text.

✨ Enjoy efficient AI

You now have a fast, physics-powered language tool that creates coherent writing and scales to huge stories effortlessly.

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AI-Generated Review

What is wave-field-llm?

Wave-field-llm is a Python LLM architecture that replaces O(n²) self-attention with O(n log n) wave equation dynamics on continuous fields, delivering within 5% of standard transformer quality on WikiText-2. It lets you train ~6M or 100M param models on datasets like OpenWebText, with massive compute savings—9x at 512 tokens, up to 367x at 32k sequences. Run benchmarks or your own training via simple scripts for causal language modeling.

Why is it gaining traction?

The hook is real scaling: transformers explode quadratically, but this hits transformer-level perplexity with linearithmic speed via physics-inspired field propagation. Heads specialize naturally (local grammar to long-range structure) with built-in diagnostics tracing energy flow. Devs notice cleaner long-context generation and physics quantities exposing bugs no profiler catches.

Who should use this?

ML researchers benchmarking efficient architectures beyond Mamba/Hyena. Teams training long-context LLMs on modest GPUs, like document QA or code gen. Anyone prototyping O(n log n) models who wants transformer quality without quadratic memory walls.

Verdict

Grab it for efficiency experiments—benchmarks hold up against standard transformers. With 17 stars and 1.0% credibility, it's early (tune hyperparameters carefully), but solid README, scripts, and physics docs lower the ramp-up. Play if scaling inference matters.

(198 words)

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