Cross-Paradigm Architectural Comparison
Generation Race: How Paradigms Compare at Runtime
Visualizing the fundamental trade-offs between serial token prediction, parallel multi-scale hierarchies, iterative score diffusion, and continuous flow ODEs.
Simulated 64-Unit Sequence Generation Benchmark
Watch how each paradigm fulfills the target sequence over time.
Next-Concept Modeling (NCM)Fastest (Parallel Hierarchy)
4 passes total0%
Flow Matching / Rectified FlowFew-Step Continuous
8–16 Euler steps0%
Discrete / Gaussian DiffusionIterative Refinement
20–50 steps0%
Autoregressive LLM (Next-Token)Memory-Bandwidth Bound
64 serial steps (1 token / step)0%
Comprehensive Paradigm Matrix
| Paradigm | Training Mechanism | Inference Latency | Key Bottleneck | Flagship Model |
|---|---|---|---|---|
| Autoregressive LLM | 100% Parallel (Causal Mask) | O(T) Serial Steps | Memory Bandwidth (Weights + KV Cache) | GPT-4 / Llama 3 |
| Next-Concept Modeling (NCM) | Multiscale RVQ + Level Parallel | O(L) Hierarchy Passes (L ≪ T) | Codebook commitment quality | NCM (Hazy / Together) |
| Discrete Diffusion (MDLM) | Absorbing [MASK] noise | O(K) Full-sequence passes | Full-length forward passes every step | LLaDA / MDLM |
| Gaussian Diffusion (DDPM) | Closed-form noise MSE | O(K) Iterative SDE steps | Curved sampling trajectories | Stable Diffusion 1.5/2 |
| Flow Matching (Rectified Flow) | Straight-line velocity regression | O(K) ODE steps (4–16 steps) | ODE discretization error at high dt | Flux.1 / SD3 |