Скачать или смотреть This AI Breakthrough Changes Everything (MDMs + Denoising Entropy)

🔥 *The Future of AI Language Generation: How Path Uncertainty is Unlocking MDMs!*

Forget rigid autoregressive models—**Masked Diffusion Models (MDMs)** generate text by denoising any fragment, in any order. But decoding path matters. For years, random or greedy unmasking hurt performance.

We reveal the key: **Path Uncertainty**.

Introducing **Denoising Entropy**—a new metric measuring uncertainty across the entire generation journey:
✅ **State Entropy**: current model confidence
✅ **Path Entropy**: cumulative uncertainty over time

Lower Path Entropy = higher-quality outputs. Empirically proven: near-linear drop in perplexity with lower entropy.

Then we turn insight into action:
🚀 *E-BON**: Best-of-N, but pick the *lowest-entropy path post-generation
🚀 **E-SMC**: Real-time particle search—resample based on entropy, evolve toward confident paths

Results:
✔️ +4% accuracy on GSM8K & MATH500
✔️ +4.1% on Countdown (40.4% vs 36.3%)
✔️ Works with any sampler—uniform, confidence-based, PC-Sampler, etc.

The breakthrough? *Uncertainty isn’t a flaw—it’s a signal**. We prove it reduces KL divergence from true data, meaning better outputs *and better alignment.

Scales to LLaDA, Open-dCoder. Efficient. Universal.

👉 This isn’t just better decoding—it’s a **paradigm shift**. Smarter paths, not bigger models.

If you’re into AI research, diffusion models, or reasoning, this is essential.

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#AI #DiffusionModels #LanguageGeneration #MDM #DenoisingEntropy #AIGeneration #LLM #AIResearch #DeepLearning
Read more on arxiv by searching for this paper: 2512.21336v1.pdf