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PhysiQuanty

AI & ML interests

Theoretical Physics, Invariant Tokenization, Standard Model of Particle Physics Applied ML (coming soon)

Recent Activity

reacted to SeaWolf-AI's post with πŸ”₯ about 5 hours ago
🧬 Darwin Family: Zero Gradient Steps, GPQA Diamond 88.89% How far can we push LLM reasoning *without* training? Our team at VIDRAFT submitted this paper to Daily Papers yesterday, and it's currently #3. Huge thanks to everyone who upvoted β€” sharing the core ideas below. πŸ”— Paper: https://huggingface.co/papers/2605.14386 πŸ”— arXiv: https://arxiv.org/abs/2605.14386 πŸ”— Model: https://huggingface.co/FINAL-Bench/Darwin-28B-Opus --- TL;DR Darwin Family is a training-free evolutionary merging framework. By recombining the weight spaces of existing LLM checkpoints β€” with zero gradient-based training β€” it reaches frontier-level reasoning. - πŸ† Darwin-28B-Opus: GPQA Diamond 88.89% - πŸ’Έ Zero gradient steps β€” not a single B200 or H200 hour needed - 🧬 Consistent gains across 4B β†’ 35B scale - πŸ”€ Cross-architecture breeding between Transformer and Mamba families - πŸ” Stable recursive multi-generation evolution #Three Core Mechanisms β‘  14-dim Adaptive Merge Genome β€” fine-grained recombination at both component level (Attention / FFN / MLP / LayerNorm / Embedding) and block level, expanding the prior evolutionary-merge search space. β‘‘ MRI-Trust Fusion β€” we diagnose each layer's reasoning contribution via an **MRI (Model Reasoning Importance)** signal and fuse it with evolutionary search through a **learnable trust parameter**. Trust the diagnostic too much and search collapses; ignore it and search becomes inefficient β€” Darwin learns the balance from data. β‘’ Architecture Mapper β€” weight-space breeding across heterogeneous families. Attention Γ— SSM crossover actually works. Why It Matters > Diagnose latent capabilities already encoded in open checkpoints, > and recombine them β€” no gradients required. Replies and critiques welcome πŸ™Œ
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