mindchain/instruct
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Karsten Kuhnke PRO
mindchain
AI & ML interests
Mechanistic Interpretability, Sparse Autoencoders, JumpReLU, Reward Modeling, RLHF, AI Alignment, Function Calling, Gemma, Nemotron
Recent Activity
upvoted
a
paper
about 1 hour ago
SmolDocling: An ultra-compact vision-language model for end-to-end
multi-modal document conversion
reacted
to
their
post
with 🧠
about 1 hour ago
The Architecture of 2026: Beyond the Token Trap 🚀
We are witnessing a tectonic shift in Transformer architecture. It’s no longer just about "predicting the next token"—it’s about executing latent plans on a high-speed data highway.
What happens when we combine DeepSeek’s stability with Google’s strategic intelligence?
1️⃣ The Infrastructure: DeepSeek’s mHC Moving from a single-lane residual stream to a multi-lane highway. Using the Birkhoff Polytope, mHC ensures mathematical stability (Identity Mapping) while routing specialized data through dedicated lanes.
2️⃣ The Intelligence: Google’s Meta-Controller An internal AI unit that lives inside the Transformer. It escapes the "Token Trap" by extracting data to create a latent plan, steering the model via Temporal Abstraction.
The Synergy: In a Topological Transformer, the Meta-Controller finally has the "dedicated lanes" it needs to steer complex reasoning without causing gradient explosions.
We aren't just making models bigger; we are making them architecturally smarter. 🧠
#MachineLearning #DeepSeek #GoogleAI #Transformer #AIArchitecture
reacted
to
their
post
with 🤝
about 1 hour ago
The Architecture of 2026: Beyond the Token Trap 🚀
We are witnessing a tectonic shift in Transformer architecture. It’s no longer just about "predicting the next token"—it’s about executing latent plans on a high-speed data highway.
What happens when we combine DeepSeek’s stability with Google’s strategic intelligence?
1️⃣ The Infrastructure: DeepSeek’s mHC Moving from a single-lane residual stream to a multi-lane highway. Using the Birkhoff Polytope, mHC ensures mathematical stability (Identity Mapping) while routing specialized data through dedicated lanes.
2️⃣ The Intelligence: Google’s Meta-Controller An internal AI unit that lives inside the Transformer. It escapes the "Token Trap" by extracting data to create a latent plan, steering the model via Temporal Abstraction.
The Synergy: In a Topological Transformer, the Meta-Controller finally has the "dedicated lanes" it needs to steer complex reasoning without causing gradient explosions.
We aren't just making models bigger; we are making them architecturally smarter. 🧠
#MachineLearning #DeepSeek #GoogleAI #Transformer #AIArchitecture
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