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Jonatan Borkowski

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upvoted a paper 5 days ago
Darwin Family: MRI-Trust-Weighted Evolutionary Merging for Training-Free Scaling of Language-Model Reasoning
reacted to SeaWolf-AI's post with 🔥 5 days 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 — sharing the core ideas below. 🔗 Paper: https://huggingface.co/papers/2605.14386 🔗 arXiv: https://arxiv.org/abs/2605.14386 🔗 Model: https://huggingface.co/FINAL-Bench/Darwin-28B-REASON 🔗 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 — with zero gradient-based training — it reaches frontier-level reasoning. - 🏆 Darwin-28B-Opus: GPQA Diamond 88.89% - 💸 Zero gradient steps — not a single B200 or H200 hour needed - 🧬 Consistent gains across 4B → 35B scale - 🔀 Cross-architecture breeding between Transformer and Mamba families - 🔁 Stable recursive multi-generation evolution #Three Core Mechanisms ① 14-dim Adaptive Merge Genome — fine-grained recombination at both component level (Attention / FFN / MLP / LayerNorm / Embedding) and block level, expanding the prior evolutionary-merge search space. ② MRI-Trust Fusion — 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 — Darwin learns the balance from data. ③ Architecture Mapper — weight-space breeding across heterogeneous families. Attention × SSM crossover actually works. Why It Matters > Diagnose latent capabilities already encoded in open checkpoints, > and recombine them — no gradients required. Replies and critiques welcome 🙌
reacted to spillai's post with 🔥 7 days ago
mm-ctx – fast, multimodal context for agents. LLM-based agents handle text incredibly well, but images, videos, or PDFs with visual content are hard to interpret. mm-ctx gives your CLI agent multi-modal skills. Try it interactively in Spaces: https://huggingface.co/spaces/vlm-run/mm-ctx Readme: https://vlm-run.github.io/mm/ PyPI: https://pypi.org/project/mm-ctx SKILL.md: https://github.com/vlm-run/skills/blob/main/skills/mm-cli-skill/SKILL.md mm-ctx is meant to feel familiar: the UNIX tools we already love (find/cat/grep/wc), rebuilt for file types LLMs can't read natively and designed to work with agents via the CLI. - mm grep "invoice #1234" ~/Downloads searches across PDFs and returns line-numbered matches - mm cat <document>.pdf returns a metadata description of the file - mm cat <photo>.jpg returns a caption of the photo - mm cat <video>.mp4 returns a caption of the video A few things we obsessed over: ⚡ Speed: Rust core for the hot paths 🏠 Local-first, BYO model: Uses any OpenAI-compatible endpoint: Ollama, vLLM/SGLang, LMStudio with any multimodal LLM (Gemma4, Qwen3.5, GLM-4.6V). 🔗 Composable: stdin + structured outputs 🤖 Drops into any agent via mm-cli-skills: Claude Code, Codex, Gemini CLI, OpenClaw. We’d love to hear your feedback! Especially on the CLI and what file types and workflows you would like to see next.
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