Tenkai Daily — March 17, 2026

Open Source Releases

• Qwen3.5-122B-A10B Quantized with MLX and NVFP4 — A 122B parameter model squeezed into a format that runs on Apple Silicon via MLX. If you’re doing local inference on a Mac, this is a practical example of NVFP4 quantization in the wild.
• DeepSeek-R1-Distill-Qwen-7B Fine-tuned with SFT and CoT — A PEFT/LoRA fine-tune that adds chain-of-thought reasoning to a distilled model, with multiple training checkpoints provided. Useful if you want to study or replicate a specific stage of SFT+CoT training.

Research Worth Reading

• Think First, Diffuse Fast: Improving Diffusion Language Model Reasoning via Autoregressive Plan Conditioning — Proposes using an autoregressive model to generate a “plan” that conditions a diffusion LM, improving multi-step reasoning without extra training. A clever hack for getting more coherent long-form generation from diffusion models.
• ILION: Deterministic Pre-Execution Safety Gates for Agentic AI Systems — Introduces a formal, deterministic layer that must approve an AI agent’s action before it executes in the real world. A serious architectural pattern for anyone building agents that interact with physical or high-stakes digital environments.
• Why Grokking Takes So Long: A First-Principles Theory of Representational Phase Transitions — Offers a theoretical explanation for the delay between a model memorizing training data and suddenly generalizing. It’s dense, but if you’re debugging training dynamics, this provides a useful lens.

AI Dev Tools

• jax-cce 0.1.0 — A memory-efficient fused cross-entropy loss for JAX that avoids creating the full logits matrix. A direct drop-in for training large models in JAX where memory is the primary bottleneck.

Community Finds

• TradingAgents — A multi-agent LLM framework specifically for financial trading strategies. It’s a concrete (if high-risk) example of orchestrating multiple LLM agents for a complex, sequential decision task.

Today’s Synthesis

Building a robust AI system isn’t just about the model; it’s about the entire inference and decision pipeline. Take the efficiency gains from Qwen3.5-122B-A10B Quantized with MLX and NVFP4 , which lets you run a massive model locally. Now, imagine deploying that model as part of an agent that takes real-world actions. That’s where the deterministic safety checks from ILION: Deterministic Pre-Execution Safety Gates for Agentic AI Systems become non-negotiable—you need a hard, verifiable gate between the model’s output and execution. Finally, to make the agent’s reasoning more reliable, you could borrow the “plan first” idea from Think First, Diffuse Fast , using a smaller, faster model to generate an explicit plan that guides the larger model’s actions. The practical takeaway: pair aggressive model compression for on-device inference with formal, non-learned safety layers and structured reasoning prompts to build systems that are both efficient and trustworthy.