Algorithm Visualization (AV) helps students build mental models by animating algorithm execution states. Recent LLM-based systems such as CODE2VIDEO generate AV videos in an end-to-end manner. However, this paradigm requires the system to simultaneously simulate algorithm flow and satisfy video rendering constraints (element layout, color schemes, etc.), a complex task that induces LLM hallucinations. This results in reduced execution success rates, element overlap, and inter-frame inconsistencies.To address these challenges, we propose ALGOGEN, a novel paradigm that decouples algorithm execution from rendering. We first introduce Visualization Trace Algebra (VTA), a monoid over algorithm visual states and operations. The LLM then generates a Python tracker that simulates algorithm flow and outputs VTA-JSON traces, a JSON encoding of VTA. For rendering, we define a Rendering Style Language (RSL) to templatize algorithm layouts. A deterministic renderer then compiles algorithm traces with RSL into Manim, LaTeX/TikZ, or Three.js outputs[Manim, TikZ, and Three.js are respectively a Python animation engine, a LaTeX vector graphics package, and a JavaScript 3D rendering library.].Evaluated on a LeetCode AV benchmark of 200 tasks, ALGOGEN achieves an average success rate improvement of 17.3% compared to end-to-end methods (99.8% vs. 82.5%). These results demonstrate that our decoupling paradigm effectively mitigates LLM hallucinations in complex AV tasks, providing a more reliable solution for automated generation of high-quality algorithm visualizations. Demo videos and code are available at: .

The Mixture of Experts (MoE) architecture enables efficient model scaling through conditional computation, where only subset of parameters are activated per input. However, this distributed architecture poses unprecedented challenges for model compression, as conventional quantization methods optimized for dense networks prove inadequate. This paper introduces a specialized quantization framework for MoE architectures, motivated by our discovery that weight matrices across expert networks exhibit distinctive channel-wise outlier distributions, necessitating a more nuanced compression approach. Through theoretical analysis incorporating Fisher Information matrices and condition number characteristics, we establish a fundamental relationship between layer functionality and quantization sensitivity, demonstrating that down-projection layers inherently demand higher precision compared to up-projection layers. Leveraging these insights, we develop an automated channel-wise quantization framework that dynamically determines optimal bit-width allocations while maintaining minimal computational overhead through efficient statistical approximations. When evaluated on the Mixtral-8x7b-v0.1 architecture, our methodology demonstrates a 3.96% improvement over existing state-of-the-art approaches across natural language understanding benchmarks, while achieving superior compression ratios.