Cannae-AI/TANIT-V0.6-4B-IT-gguf
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Cannae AI
CannaeAI
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AI & ML interests
Cannae Ai,a startup,Combining development and research in the fields of General purpose and specific use case AI,soon to operate in the agentic ai field.
Recent Activity
updated a model 15 days ago
Cannae-AI/TANIT-V0.6-4B-IT-gguf reacted to SeaWolf-AI's post with š„ 3 months ago
š§¬ 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 ā sharing the core ideas below.
š Paper: https://huggingface.co/papers/2605.14386
š arXiv: https://arxiv.org/abs/2605.14386
š Model: https://huggingface.co/FINAL-Bench/Darwin-28B-REASON
š 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 ā with zero
gradient-based training ā it reaches frontier-level reasoning.
- š Darwin-28B-Opus: GPQA Diamond 88.89%
- šø Zero gradient steps ā not a single B200 or H200 hour needed
- š§¬ Consistent gains across 4B ā 35B scale
- š Cross-architecture breeding between Transformer and Mamba families
- š Stable recursive multi-generation evolution
#Three Core Mechanisms
ā 14-dim Adaptive Merge Genome ā fine-grained recombination at both
component level (Attention / FFN / MLP / LayerNorm / Embedding) and block
level, expanding the prior evolutionary-merge search space.
ā” MRI-Trust Fusion ā 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 ā Darwin learns the balance from data.
ā¢ Architecture Mapper ā weight-space breeding across heterogeneous
families. Attention Ć SSM crossover actually works.
Why It Matters
> Diagnose latent capabilities already encoded in open checkpoints,
> and recombine them ā no gradients required.
Replies and critiques welcome šOrganizations
updated a
model 15 days ago
reacted to SeaWolf-AI's post with š„ 3 months ago
Post
5440
š§¬ 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 ā sharing the core ideas below.
š Paper: Darwin Family: MRI-Trust-Weighted Evolutionary Merging for Training-Free Scaling of Language-Model Reasoning (2605.14386)
š arXiv: https://arxiv.org/abs/2605.14386
š Model: FINAL-Bench/Darwin-28B-REASON
š Model: 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 ā with zero
gradient-based training ā it reaches frontier-level reasoning.
- š Darwin-28B-Opus: GPQA Diamond 88.89%
- šø Zero gradient steps ā not a single B200 or H200 hour needed
- š§¬ Consistent gains across 4B ā 35B scale
- š Cross-architecture breeding between Transformer and Mamba families
- š Stable recursive multi-generation evolution
#Three Core Mechanisms
ā 14-dim Adaptive Merge Genome ā fine-grained recombination at both
component level (Attention / FFN / MLP / LayerNorm / Embedding) and block
level, expanding the prior evolutionary-merge search space.
ā” MRI-Trust Fusion ā 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 ā Darwin learns the balance from data.
ā¢ Architecture Mapper ā weight-space breeding across heterogeneous
families. Attention Ć SSM crossover actually works.
Why It Matters
> Diagnose latent capabilities already encoded in open checkpoints,
> and recombine them ā no gradients required.
Replies and critiques welcome š
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 ā sharing the core ideas below.
š Paper: Darwin Family: MRI-Trust-Weighted Evolutionary Merging for Training-Free Scaling of Language-Model Reasoning (2605.14386)
š arXiv: https://arxiv.org/abs/2605.14386
š Model: FINAL-Bench/Darwin-28B-REASON
š Model: 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 ā with zero
gradient-based training ā it reaches frontier-level reasoning.
- š Darwin-28B-Opus: GPQA Diamond 88.89%
- šø Zero gradient steps ā not a single B200 or H200 hour needed
- š§¬ Consistent gains across 4B ā 35B scale
- š Cross-architecture breeding between Transformer and Mamba families
- š Stable recursive multi-generation evolution
#Three Core Mechanisms
ā 14-dim Adaptive Merge Genome ā fine-grained recombination at both
component level (Attention / FFN / MLP / LayerNorm / Embedding) and block
level, expanding the prior evolutionary-merge search space.
ā” MRI-Trust Fusion ā 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 ā Darwin learns the balance from data.
ā¢ Architecture Mapper ā weight-space breeding across heterogeneous
families. Attention Ć SSM crossover actually works.
Why It Matters
> Diagnose latent capabilities already encoded in open checkpoints,
> and recombine them ā no gradients required.
Replies and critiques welcome š
- 3 replies
reacted to victor's post with š„ 4 months ago
Post
6240
Want to share my enthusiasm for zai-org/GLM-5.1 here too š„
I think we have it: our open source Claude Code = GLM-5.1 + Pi (https://pi.dev/) - Built a Three.js racing game to eval and it's extremely impressive. Thoughts:
- One-shot car physics with real drift mechanics (this is hard)
- My fav part: Awesome at self iterating (with no vision!) created 20+ Bun.WebView debugging tools to drive the car programmatically and read game state. Proved a winding bug with vector math without ever seeing the screen
- 531-line racing AI in a single write: 4 personalities, curvature map, racing lines, tactical drifting. Built telemetry tools to compare player vs AI speed curves and data-tuned parameters
- All assets from scratch: 3D models, procedural textures, sky shader, engine sounds, spatial AI audio!
- Can do hard math: proved road normals pointed DOWN via vector cross products, computed track curvature normalized by arc length to tune AI cornering speed
You are going to hear about this model a lot in the next months - open source let's go - and thanks z-aišš
I think we have it: our open source Claude Code = GLM-5.1 + Pi (https://pi.dev/) - Built a Three.js racing game to eval and it's extremely impressive. Thoughts:
- One-shot car physics with real drift mechanics (this is hard)
- My fav part: Awesome at self iterating (with no vision!) created 20+ Bun.WebView debugging tools to drive the car programmatically and read game state. Proved a winding bug with vector math without ever seeing the screen
- 531-line racing AI in a single write: 4 personalities, curvature map, racing lines, tactical drifting. Built telemetry tools to compare player vs AI speed curves and data-tuned parameters
- All assets from scratch: 3D models, procedural textures, sky shader, engine sounds, spatial AI audio!
- Can do hard math: proved road normals pointed DOWN via vector cross products, computed track curvature normalized by arc length to tune AI cornering speed
You are going to hear about this model a lot in the next months - open source let's go - and thanks z-aišš
- 5 replies
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