[AI Minor News Flash] Hack the Brain of LLMs! ‘RYS’ Technique Enhances Performance by Duplicating Mid-Layer, Proven Effective on Latest Models

📰 News Summary

• Performance Boost Without Training: The ‘RYS’ (Repeat Your Self) technique, which duplicates mid-layers of LLMs, has been confirmed effective on the latest Qwen3.5-27B.
• Proof of Three-Phase Structure: Experiments have directly demonstrated that the model operates in three stages: “Encoding (initial),” “Inference (mid),” and “Decoding (final).”
• Transcending Language with a Common Thinking Space: At the mid-layer, regardless of superficial language differences like English or Chinese, a ‘common thinking space’ processes concepts with remarkably high similarity if they share the same meaning.

💡 Key Points

• Scalability Confirmed: The RYS technique, previously discovered in Qwen2-72B, has proven applicable even in the more compact and highly engineered 27B model.
• Massive Optimization Efforts: Thorough validation involved scoring 3,024 candidates and 2 million configurations using a surrogate model to identify the optimal layer structure.
• Language-Independent Abstraction: In mid-layers (around layer 15 and beyond), the emphasis shifts from “which language” to “what is being talked about,” allowing for greater abstraction across different languages.

🦈 Shark’s Eye (Curator’s Perspective)

The ‘RYS’ technique is just too cool—optimizing the brain’s structure to enhance performance without updating weights or additional training, just pure mathematical probing! What’s mind-blowing is the experimental proof that mid-layers function as a ‘universal thinking space.’ When comparing facts from English and Chinese, the language barrier fades away, extracting only the ‘pure essence of meaning.’ This abstract layer is undeniably at the core of LLM intelligence! This approach, enhancing existing models by merely ‘adding layers,’ shines as a beacon of hope for individual developers with limited resources!

🚀 What’s Next?

As the ‘functional anatomy’ of models becomes clearer, techniques for pinpointing and enhancing layers responsible for specific capabilities (like logical reasoning) will become more mainstream. In multilingual models, hacks at the ‘conceptual level’ that do not rely on specific languages will accelerate.

💬 One Sharky Comment

Tinkering with the AI brain to make it smarter is pure cyberpunk magic! Getting stronger without any training? That’s unbeatable value! 🦈🔥

📚 Terminology

• RYS (Repeat Your Self): A technique that boosts model parameters and performance by duplicating specific layers (mainly mid-layers) without additional learning.

• Cosine Similarity: A measure indicating how similarly two vectors point in the same direction, used to assess how ‘similar’ internal representations of AI are.

• Transformers: The architecture underlying current LLMs, utilizing the attention mechanism to learn correlations within data.

• Source: LLM Neuroanatomy II: Modern LLM Hacking and Hints of a Universal Language?