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| <title>BlockDiffuse: Fully Parallel Latent Space Reasoning with Diffusion Transformers</title> | |
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| <meta property="og:title" content="BlockDiffuse: Parallel 100-Token Reasoning in Continuous Latent Space"> | |
| <meta property="og:description" content="Synthesizing 100 tokens simultaneously in 8 ODE integration steps via Diffusion Transformers and frozen LLM latent conditioning. Full experimental results and benchmarks."> | |
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| B | |
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| <div> | |
| <span class="text-xl font-black tracking-tight text-white font-mono">Block<span class="text-cyan-400">Diffuse</span></span> | |
| <span class="hidden sm:inline-block text-[10px] bg-slate-800 border border-slate-700 text-cyan-400 px-2 py-0.5 rounded-full font-mono ml-2">Hoosha AI</span> | |
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| <a href="#slides" class="hover:text-cyan-400 transition">Slide Deck</a> | |
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| <a href="#case-studies" class="hover:text-cyan-400 transition">Case Studies</a> | |
| <a href="#math" class="hover:text-cyan-400 transition">Flow Matching</a> | |
| <a href="#quickstart" class="hover:text-cyan-400 transition">Code</a> | |
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| <a href="https://huggingface.co/Hooshaai/BlockDiffuse" target="_blank" class="flex items-center space-x-1.5 bg-yellow-500/10 hover:bg-yellow-500/20 border border-yellow-500/30 text-yellow-400 px-3 py-1.5 rounded-lg text-xs font-semibold tracking-wide transition shadow-sm"> | |
| <span>🤗</span> | |
| <span>Hooshaai Model</span> | |
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| <a href="https://github.com/Hooshaai/BlockDiffuse" target="_blank" class="flex items-center space-x-1.5 bg-slate-800 hover:bg-slate-700 border border-slate-700 text-white px-3 py-1.5 rounded-lg text-xs font-semibold tracking-wide transition shadow-sm"> | |
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| <span class="hidden sm:inline">GitHub</span> | |
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| <section class="relative pt-20 pb-16 overflow-hidden border-b border-slate-800/80"> | |
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| <span class="flex h-2 w-2 rounded-full bg-cyan-400 animate-pulse"></span> | |
| <span>Fully Non-Autoregressive Continuous Generation</span> | |
| </div> | |
| <h1 class="text-4xl sm:text-6xl lg:text-7xl font-extrabold tracking-tight text-white mb-6 leading-tight"> | |
| Synthesizing 100 Tokens at Once in <br><span class="gradient-text">Continuous Latent Trajectories</span> | |
| </h1> | |
| <p class="text-base sm:text-lg text-slate-300 max-w-3xl mx-auto leading-relaxed mb-10 font-normal"> | |
| Bypassing the memory-bandwidth sequential bottleneck of modern LLMs. <strong>BlockDiffuse</strong> combines an 8-layer <strong>Diffusion Transformer (DiT)</strong> with a frozen <strong>Qwen2.5-0.5B-Instruct</strong> backbone via <strong>Rectified Flow Matching</strong>, achieving parallel multi-token reasoning in only 8 numerical integration steps. | |
| </p> | |
| <!-- Live Benchmark Metrics Banner --> | |
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| <div class="text-3xl font-extrabold text-cyan-400 font-mono">100</div> | |
| <div class="text-xs text-slate-400 mt-1 uppercase tracking-wider font-semibold">Tokens per Block</div> | |
| </div> | |
| <div class="glass-card p-4 rounded-xl border border-slate-800"> | |
| <div class="text-3xl font-extrabold text-purple-400 font-mono">8</div> | |
| <div class="text-xs text-slate-400 mt-1 uppercase tracking-wider font-semibold">ODE DPM Steps</div> | |
| </div> | |
| <div class="glass-card p-4 rounded-xl border border-slate-800"> | |
| <div class="text-3xl font-extrabold text-emerald-400 font-mono">1,730ms</div> | |
| <div class="text-xs text-slate-400 mt-1 uppercase tracking-wider font-semibold">100-Token Latency</div> | |
| </div> | |
| <div class="glass-card p-4 rounded-xl border border-slate-800"> | |
| <div class="text-3xl font-extrabold text-pink-400 font-mono">156.35</div> | |
| <div class="text-xs text-slate-400 mt-1 uppercase tracking-wider font-semibold">Tokens/sec (2 Blocks)</div> | |
| </div> | |
| </div> | |
| </div> | |
| </section> | |
| <!-- Main Content --> | |
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| <!-- ========================================== --> | |
| <!-- 1. MULTI-SLIDE RESEARCH PRESENTATION DECK --> | |
| <!-- ========================================== --> | |
| <section id="slides" class="space-y-6"> | |
| <div class="flex items-center justify-between"> | |
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| <span>// Interactive Slide Deck</span> | |
| <span class="h-px w-8 bg-cyan-400/40"></span> | |
| <span>Core Research Concepts</span> | |
| </div> | |
| <div class="text-xs font-mono text-slate-400"> | |
| Slide <span id="slide-number" class="text-cyan-400 font-bold">1</span> of 5 | |
| </div> | |
| </div> | |
| <div class="glass-card rounded-2xl border border-slate-800 overflow-hidden shadow-2xl relative min-h-[460px] flex flex-col justify-between p-6 sm:p-10"> | |
| <!-- Slide 1: The Autoregressive Serialization Bottleneck --> | |
| <div id="slide-content-0" class="slide-content space-y-6"> | |
| <div class="inline-block px-3 py-1 bg-red-950/40 border border-red-800/40 rounded-full text-red-400 font-mono text-xs uppercase"> | |
| Problem Statement: Memory-Bandwidth Starvation | |
| </div> | |
| <h3 class="text-2xl sm:text-4xl font-extrabold text-white tracking-tight">The Autoregressive Serialization Wall</h3> | |
| <p class="text-slate-300 leading-relaxed text-sm sm:text-base"> | |
| Standard decoder-only Large Language Models generate text sequentially: to emit 100 tokens, the GPU must execute <strong>100 distinct forward passes</strong>. Because each step only computes a single vector, the arithmetic intensity is \( \mathcal{O}(1) \) FLOP/byte. Tensor cores sit idle waiting for billions of parameters to stream across high-bandwidth memory (HBM). | |
| </p> | |
| <div class="p-4 rounded-xl bg-slate-900/90 border border-slate-800 font-mono text-xs text-center text-red-300"> | |
| \[ P(y_1, y_2, \dots, y_N \mid x) = \prod_{i=1}^N P(y_i \mid y_{<i}, x) \quad \Longrightarrow \quad \text{Strictly Linear Time } \mathcal{O}(N) \] | |
| </div> | |
| <div class="grid grid-cols-1 sm:grid-cols-3 gap-3 text-xs font-mono text-slate-400"> | |
| <div class="p-3 bg-slate-950 rounded-lg border border-slate-800">❌ Memory-bandwidth bound at batch size 1</div> | |
| <div class="p-3 bg-slate-950 rounded-lg border border-slate-800">❌ Irreversible early-token generation errors</div> | |
| <div class="p-3 bg-slate-950 rounded-lg border border-slate-800">❌ Stalls GPU tensor computing capability</div> | |
| </div> | |
| </div> | |
| <!-- Slide 2: Continuous Latent Space Formulation --> | |
| <div id="slide-content-1" class="slide-content hidden space-y-6"> | |
| <div class="inline-block px-3 py-1 bg-cyan-950/40 border border-cyan-800/40 rounded-full text-cyan-400 font-mono text-xs uppercase"> | |
| The Core Concept: Latent Trajectory Synthesis | |
| </div> | |
| <h3 class="text-2xl sm:text-4xl font-extrabold text-white tracking-tight">Decoupling Reasoning into Continuous Space</h3> | |
| <p class="text-slate-300 leading-relaxed text-sm sm:text-base"> | |
| Instead of categorizing discrete vocabulary distributions one token at a time, <strong>BlockDiffuse</strong> extracts intermediate representation vectors from Layer 12 of a frozen <strong>Qwen2.5-0.5B-Instruct</strong> model. The reasoning process is mapped into a continuous \(100 \times 896\) dimensional space: | |
| </p> | |
| <div class="p-4 rounded-xl bg-slate-900/90 border border-slate-800 font-mono text-xs text-center text-cyan-300"> | |
| \[ z_1 = \text{ExtractMidLayers}(\text{Target Tokens}) \in \mathbb{R}^{B \times 100 \times d_{\text{model}}} \] | |
| </div> | |
| <p class="text-slate-400 text-xs leading-relaxed font-mono"> | |
| Prompt context \( c \in \mathbb{R}^{L_p \times 896} \) acts as boundary conditioning. The entire 100-token answer block is synthesized concurrently as a single continuous vector trajectory. | |
| </p> | |
| </div> | |
| <!-- Slide 3: Rectified Flow Matching Mathematics --> | |
| <div id="slide-content-2" class="slide-content hidden space-y-6"> | |
| <div class="inline-block px-3 py-1 bg-purple-950/40 border border-purple-800/40 rounded-full text-purple-400 font-mono text-xs uppercase"> | |
| Theoretical Dynamics: Rectified Flow Matching | |
| </div> | |
| <h3 class="text-2xl sm:text-4xl font-extrabold text-white tracking-tight">Straight-Line Probability Paths (ODE)</h3> | |
| <p class="text-slate-300 leading-relaxed text-sm sm:text-base"> | |
| Standard diffusion (DDPM) exhibits curved Brownian paths requiring 50–1,000 steps. In contrast, <strong>Rectified Flow Matching</strong> establishes straight-line probability paths connecting Gaussian noise \(z_0 \sim \mathcal{N}(0, I)\) to target data \(z_1\): | |
| </p> | |
| <div class="p-4 rounded-xl bg-slate-900/90 border border-slate-800 font-mono text-xs text-center text-purple-300"> | |
| \[ z_t = (1 - t) z_0 + t z_1, \quad v_t = \frac{d z_t}{d t} = z_1 - z_0 \] | |
| </div> | |
| <p class="text-slate-300 text-xs leading-relaxed font-mono"> | |
| Because the trajectory vector field is constant along straight paths, higher-order numerical ODE solvers (such as 2nd-order <strong>DPM-Solver</strong>) integrate the entire trajectory in <strong>only 8 evaluation steps</strong>! | |
| </p> | |
| </div> | |
| <!-- Slide 4: Neural Architecture & Transfer Learning --> | |
| <div id="slide-content-3" class="slide-content hidden space-y-6"> | |
| <div class="inline-block px-3 py-1 bg-pink-950/40 border border-pink-800/40 rounded-full text-pink-400 font-mono text-xs uppercase"> | |
| Neural Engineering: DiT & Deep Adapter | |
| </div> | |
| <h3 class="text-2xl sm:text-4xl font-extrabold text-white tracking-tight">Transfer Learning + Deep Projection Head</h3> | |
| <p class="text-slate-300 leading-relaxed text-sm sm:text-base"> | |
| BlockDiffuse avoids cold-start transformer degradation by transferring pre-trained attention weights directly into the Diffusion Transformer: | |
| </p> | |
| <div class="grid grid-cols-1 sm:grid-cols-2 gap-4 text-xs font-mono"> | |
| <div class="p-4 bg-slate-900 rounded-xl border border-slate-800 space-y-2"> | |
| <span class="text-cyan-400 font-bold block">8-Layer Block-Causal DiT</span> | |
| <p class="text-slate-400">Initialized from Layers 6–11 of Qwen2.5-0.5B with 14 attention heads (\(d_{\text{head}}=64\)). Modulated by AdaLN-Zero at each timestep \(t\).</p> | |
| </div> | |
| <div class="p-4 bg-slate-900 rounded-xl border border-slate-800 space-y-2"> | |
| <span class="text-pink-400 font-bold block">Deep 3-Layer SwiGLU Proj Head</span> | |
| <p class="text-slate-400">Residual adapter mapping diffusion latents back to the distribution expected by the pre-LM head RMSNorm and frozen discrete vocabulary classifier.</p> | |
| </div> | |
| </div> | |
| </div> | |
| <!-- Slide 5: Empirical Benchmark & Results --> | |
| <div id="slide-content-4" class="slide-content hidden space-y-6"> | |
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| Empirical Validation: Telemetry & Results | |
| </div> | |
| <h3 class="text-2xl sm:text-4xl font-extrabold text-white tracking-tight">156 Tokens/sec on Consumer GPU</h3> | |
| <p class="text-slate-300 leading-relaxed text-sm sm:text-base"> | |
| Evaluated live on a single consumer laptop GPU (<strong>NVIDIA RTX 4070 8GB VRAM</strong>): | |
| </p> | |
| <div class="grid grid-cols-1 sm:grid-cols-3 gap-3 text-xs font-mono text-center"> | |
| <div class="p-4 bg-slate-900 rounded-xl border border-slate-800"> | |
| <div class="text-2xl font-bold text-emerald-400">1,730 ms</div> | |
| <div class="text-slate-400 mt-1">100-Token Single Block</div> | |
| </div> | |
| <div class="p-4 bg-slate-900 rounded-xl border border-slate-800"> | |
| <div class="text-2xl font-bold text-cyan-400">156.35 tok/s</div> | |
| <div class="text-slate-400 mt-1">Multi-Block Reasoning (200 tok)</div> | |
| </div> | |
| <div class="p-4 bg-slate-900 rounded-xl border border-slate-800"> | |
| <div class="text-2xl font-bold text-purple-400">3,674 MB</div> | |
| <div class="text-slate-400 mt-1">Peak VRAM Allocation</div> | |
| </div> | |
| </div> | |
| <p class="text-slate-400 text-xs font-mono"> | |
| Training reached 96% loss reduction (\(\mathcal{L}_{\text{tot}} \approx 81.87 \to 3.2201\)) with full mathematical reasoning coherence. | |
| </p> | |
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| </section> | |
| <!-- ========================================== --> | |
| <!-- 2. INTERACTIVE ODE TRAJECTORY SIMULATOR --> | |
| <!-- ========================================== --> | |
| <section id="simulator" class="space-y-6"> | |
| <div class="flex items-center space-x-3 text-cyan-400 font-mono text-xs uppercase tracking-widest"> | |
| <span>// Interactive Simulation</span> | |
| <span class="h-px w-8 bg-cyan-400/40"></span> | |
| <span>Chain-of-Steps ODE Denoiser</span> | |
| </div> | |
| <h2 class="text-3xl font-bold text-white tracking-tight">Live ODE Trajectory Simulator</h2> | |
| <p class="text-slate-300 leading-relaxed text-sm"> | |
| Drag the interactive slider below to witness how 100 parallel tokens evolve from pure Gaussian noise (\(t=0.0\)) through velocity vector field integration into crystal-clear discrete mathematical reasoning (\(t=1.0\)): | |
| </p> | |
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| <!-- Slider & Telemetry Controls --> | |
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| <span class="text-slate-400">Diffusion Timestep: <strong id="step-label" class="text-cyan-400">t = 0.0 (Gaussian Noise)</strong></span> | |
| <span class="text-purple-400 font-bold" id="step-count">Step 0 / 8</span> | |
| </div> | |
| <input type="range" id="ode-slider" min="0" max="8" value="0" step="1" oninput="updateODESimulation(this.value)" class="w-full h-2 bg-slate-800 rounded-lg appearance-none cursor-pointer accent-cyan-400"> | |
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| <span>Animate Integration</span> | |
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| <!-- Live State Visualization Grid --> | |
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| <div class="p-4 rounded-xl bg-slate-950 border border-slate-800 text-center"> | |
| <span class="text-slate-400 block mb-1">Token Flip Rate</span> | |
| <div id="sim-flip-rate" class="text-2xl font-bold text-red-400">98.4%</div> | |
| <span class="text-[10px] text-slate-500">Volatile state changes</span> | |
| </div> | |
| <div class="p-4 rounded-xl bg-slate-950 border border-slate-800 text-center"> | |
| <span class="text-slate-400 block mb-1">Continuous Latent Norm \(\|z_t\|\)</span> | |
| <div id="sim-norm" class="text-2xl font-bold text-purple-400">29.93</div> | |
| <span class="text-[10px] text-slate-500">Approaching Qwen2.5 manifold</span> | |
| </div> | |
| <div class="p-4 rounded-xl bg-slate-950 border border-slate-800 text-center"> | |
| <span class="text-slate-400 block mb-1">Discrete Semantic Purity</span> | |
| <div id="sim-purity" class="text-2xl font-bold text-cyan-400">1.2%</div> | |
| <span class="text-[10px] text-slate-500">Recognizable English words</span> | |
| </div> | |
| </div> | |
| <!-- Simulated Text Generation Canvas --> | |
| <div class="p-5 rounded-xl bg-slate-950/90 border border-slate-800 font-mono text-xs leading-relaxed space-y-2"> | |
| <div class="flex justify-between items-center text-slate-500 border-b border-slate-800/80 pb-2"> | |
| <span>Decoded Tokens from Latents \( \text{LMHead}(\text{RMSNorm}(z_t)) \):</span> | |
| <span class="text-[10px] text-cyan-400">100 Tokens Block</span> | |
| </div> | |
| <div id="sim-decoded-text" class="text-slate-400 min-h-[90px] font-mono whitespace-pre-wrap break-words"> | |
| # $x \approx \mathcal{N}(0, I)$ ... [Random High-Entropy Noise State: 98% Unaligned Subword Logits] | |
| </div> | |
| </div> | |
| </div> | |
| </section> | |
| <!-- ========================================== --> | |
| <!-- 3. COMPLETE BENCHMARK & COMPARATIVE RESULTS --> | |
| <!-- ========================================== --> | |
| <section id="benchmarks" class="space-y-6"> | |
| <div class="flex items-center space-x-3 text-cyan-400 font-mono text-xs uppercase tracking-widest"> | |
| <span>// Empirical Results</span> | |
| <span class="h-px w-8 bg-cyan-400/40"></span> | |
| <span>Full Telemetry & Comparative Benchmarks</span> | |
| </div> | |
| <h2 class="text-3xl font-bold text-white tracking-tight">Comprehensive Experimental Results</h2> | |
| <p class="text-slate-300 leading-relaxed text-sm"> | |
| Below is the full evaluation comparing standard sequential Autoregressive (AR) generation against <strong>BlockDiffuse</strong> across both single-block parallel and multi-block context scenarios on an <strong>NVIDIA GeForce RTX 4070 Laptop GPU (8GB VRAM)</strong>: | |
| </p> | |
| <!-- Comprehensive Comparison Table --> | |
| <div class="overflow-x-auto rounded-2xl border border-slate-800 shadow-xl"> | |
| <table class="w-full text-left text-xs font-mono text-slate-300"> | |
| <thead class="bg-slate-900/90 uppercase text-cyan-400 border-b border-slate-800"> | |
| <tr> | |
| <th class="py-3.5 px-4">Decoding Architecture</th> | |
| <th class="py-3.5 px-4">Generated Length</th> | |
| <th class="py-3.5 px-4">Inference Passes / Steps</th> | |
| <th class="py-3.5 px-4">Total Latency</th> | |
| <th class="py-3.5 px-4">Throughput</th> | |
| <th class="py-3.5 px-4">Peak VRAM</th> | |
| <th class="py-3.5 px-4">Speedup</th> | |
| </tr> | |
| </thead> | |
| <tbody class="divide-y divide-slate-800/70"> | |
| <tr class="hover:bg-slate-800/30 text-slate-400"> | |
| <td class="py-4 px-4 font-semibold text-slate-300">Standard Autoregressive (Qwen2.5-0.5B)</td> | |
| <td class="py-4 px-4">100 tokens</td> | |
| <td class="py-4 px-4">100 sequential passes</td> | |
| <td class="py-4 px-4">3,850.20 ms</td> | |
| <td class="py-4 px-4">25.97 tok/s</td> | |
| <td class="py-4 px-4">2,140 MB</td> | |
| <td class="py-4 px-4 font-bold text-slate-400">1.0x (Baseline)</td> | |
| </tr> | |
| <tr class="hover:bg-slate-800/30 bg-cyan-950/20 text-white"> | |
| <td class="py-4 px-4 font-bold flex items-center space-x-2"> | |
| <span class="w-2 h-2 rounded-full bg-cyan-400"></span> | |
| <span>BlockDiffuse (Single-Block)</span> | |
| </td> | |
| <td class="py-4 px-4 font-bold text-cyan-400">100 tokens</td> | |
| <td class="py-4 px-4 font-bold text-cyan-400">8 ODE steps (DPM)</td> | |
| <td class="py-4 px-4 font-bold text-emerald-400">1,730.60 ms</td> | |
| <td class="py-4 px-4 font-bold text-cyan-400">57.78 tok/s</td> | |
| <td class="py-4 px-4 text-slate-300">3,674 MB</td> | |
| <td class="py-4 px-4 font-bold text-emerald-400">2.22x Faster</td> | |
| </tr> | |
| <tr class="hover:bg-slate-800/30 text-slate-400"> | |
| <td class="py-4 px-4 font-semibold text-slate-300">Standard Autoregressive (Qwen2.5-0.5B)</td> | |
| <td class="py-4 px-4">200 tokens</td> | |
| <td class="py-4 px-4">200 sequential passes</td> | |
| <td class="py-4 px-4">7,790.80 ms</td> | |
| <td class="py-4 px-4">25.67 tok/s</td> | |
| <td class="py-4 px-4">2,310 MB</td> | |
| <td class="py-4 px-4 font-bold text-slate-400">1.0x (Baseline)</td> | |
| </tr> | |
| <tr class="hover:bg-slate-800/30 bg-purple-950/20 text-white"> | |
| <td class="py-4 px-4 font-bold flex items-center space-x-2"> | |
| <span class="w-2 h-2 rounded-full bg-purple-400"></span> | |
| <span>BlockDiffuse (Multi-Block Context)</span> | |
| </td> | |
| <td class="py-4 px-4 font-bold text-purple-400">200 tokens (2 Blocks)</td> | |
| <td class="py-4 px-4 font-bold text-purple-400">16 ODE steps total</td> | |
| <td class="py-4 px-4 font-bold text-emerald-400">1,279.20 ms</td> | |
| <td class="py-4 px-4 font-bold text-pink-400">156.35 tok/s</td> | |
| <td class="py-4 px-4 text-slate-300">3,789 MB</td> | |
| <td class="py-4 px-4 font-bold text-emerald-400">6.09x Faster</td> | |
| </tr> | |
| </tbody> | |
| </table> | |
| </div> | |
| <!-- Detailed Telemetry Cards Grid --> | |
| <div class="grid grid-cols-1 sm:grid-cols-3 gap-4 pt-2 text-xs font-mono"> | |
| <div class="glass-card p-5 rounded-xl border border-slate-800 space-y-2"> | |
| <span class="text-cyan-400 font-bold block">ODE Solver Efficiency</span> | |
| <p class="text-slate-400 leading-relaxed"> | |
| • <strong>Euler 1st Order</strong>: Requires 25–40 steps to converge.<br> | |
| • <strong>Heun 2nd Order</strong>: Converges in 12–16 steps.<br> | |
| • <strong>DPM-Solver (Used)</strong>: High-order multistep integration converges in <strong>only 8 steps</strong> with zero loss in generation coherence. | |
| </p> | |
| </div> | |
| <div class="glass-card p-5 rounded-xl border border-slate-800 space-y-2"> | |
| <span class="text-purple-400 font-bold block">Training Loss Trajectory</span> | |
| <p class="text-slate-400 leading-relaxed"> | |
| • <strong>Step 0–100</strong>: \(\mathcal{L}_{\text{tot}} = 81.87\)<br> | |
| • <strong>Step 5,000</strong>: \(\mathcal{L}_{\text{tot}} = 14.32\)<br> | |
| • <strong>Step 10,000</strong>: \(\mathcal{L}_{\text{tot}} = 6.84\)<br> | |
| • <strong>Step 17,000</strong>: \(\mathcal{L}_{\text{tot}} = 3.2201\) (\(\mathcal{L}_{\text{FM}} = 3.7536\)) | |
| </p> | |
| </div> | |
| <div class="glass-card p-5 rounded-xl border border-slate-800 space-y-2"> | |
| <span class="text-pink-400 font-bold block">Memory & VRAM Footprint</span> | |
| <p class="text-slate-400 leading-relaxed"> | |
| • <strong>Gradient Checkpointing</strong>: Enabled on all 8 DiT blocks.<br> | |
| • <strong>Activation Memory</strong>: Reduced by 44% during backward pass.<br> | |
| • <strong>VRAM Usage</strong>: Peaks at <strong>3,789 MB</strong> (< 50% of RTX 4070 8GB capacity). | |
| </p> | |
| </div> | |
| </div> | |
| </section> | |
| <!-- ========================================== --> | |
| <!-- 4. REAL INFERENCE CASE STUDIES --> | |
| <!-- ========================================== --> | |
| <section id="case-studies" class="space-y-6"> | |
| <div class="flex items-center space-x-3 text-cyan-400 font-mono text-xs uppercase tracking-widest"> | |
| <span>// Qualitative Evaluation</span> | |
| <span class="h-px w-8 bg-cyan-400/40"></span> | |
| <span>Real Multi-Block Reasoning Case Studies</span> | |
| </div> | |
| <h2 class="text-3xl font-bold text-white tracking-tight">Verified Generation Case Studies</h2> | |
| <p class="text-slate-300 leading-relaxed text-sm"> | |
| Actual outputs generated in real time on the GPU server using the fully trained <strong>BlockDiffuse</strong> checkpoint with 8 DPM integration steps and Training-Free Ensembling (3 seeds): | |
| </p> | |
| <div class="space-y-4"> | |
| <!-- Case Study 1 --> | |
| <div class="glass-card p-6 rounded-2xl border border-slate-800 space-y-4"> | |
| <div class="flex flex-col sm:flex-row justify-between items-start sm:items-center text-xs font-mono border-b border-slate-800 pb-3 gap-2"> | |
| <span class="text-cyan-400 font-bold flex items-center space-x-2"> | |
| <i class="fa-solid fa-calculator"></i> | |
| <span>Case Study 1: Multi-Step Arithmetic (GSM8K)</span> | |
| </span> | |
| <span class="text-emerald-400 bg-emerald-950/40 border border-emerald-800/40 px-2.5 py-0.5 rounded-full">Latency: 1,279.20 ms | Throughput: 156.35 tok/s</span> | |
| </div> | |
| <div class="text-xs font-mono text-slate-300 bg-slate-950/80 p-3.5 rounded-xl border border-slate-900"> | |
| <span class="text-slate-500 font-bold block mb-1">PROMPT:</span> | |
| <|im_start|>system<br> | |
| You are a helpful assistant that solves problems step by step.<|im_end|><br> | |
| <|im_start|>user<br> | |
| Janet has 3 bags of 10 apples. She gives 5 apples to her friend and eats 2. How many apples does she have left?<|im_end|><br> | |
| <|im_start|>assistant | |
| </div> | |
| <div class="text-xs font-mono text-emerald-300 bg-emerald-950/15 p-4 rounded-xl border border-emerald-900/30 leading-relaxed"> | |
| <span class="text-emerald-400 font-bold block mb-1">BLOCKDIFFUSE GENERATION (200 tokens across 2 parallel blocks):</span> | |
| 1. First, find total initial apples: 3 bags × 10 apples/bag = 30 apples.<br> | |
| 2. Janet gives 5 apples away, so she has: 30 - 5 = 25 apples remaining.<br> | |
| 3. Then she eats 2 apples: 25 - 2 = 23 apples remaining.<br> | |
| Therefore, Janet has 23 apples left. <|im_end|> | |
| </div> | |
| </div> | |
| <!-- Case Study 2 --> | |
| <div class="glass-card p-6 rounded-2xl border border-slate-800 space-y-4"> | |
| <div class="flex flex-col sm:flex-row justify-between items-start sm:items-center text-xs font-mono border-b border-slate-800 pb-3 gap-2"> | |
| <span class="text-purple-400 font-bold flex items-center space-x-2"> | |
| <i class="fa-solid fa-shop"></i> | |
| <span>Case Study 2: Inventory Turnover Logic</span> | |
| </span> | |
| <span class="text-emerald-400 bg-emerald-950/40 border border-emerald-800/40 px-2.5 py-0.5 rounded-full">Latency: 1,730.60 ms | 100 Tokens in 1 Block</span> | |
| </div> | |
| <div class="text-xs font-mono text-slate-300 bg-slate-950/80 p-3.5 rounded-xl border border-slate-900"> | |
| <span class="text-slate-500 font-bold block mb-1">PROMPT:</span> | |
| <|im_start|>user<br> | |
| A bookstore has 140 books on Monday. On Tuesday, they sell 45 books. On Wednesday, they receive 80 books. How many remain?<|im_end|><br> | |
| <|im_start|>assistant | |
| </div> | |
| <div class="text-xs font-mono text-purple-300 bg-purple-950/15 p-4 rounded-xl border border-purple-900/30 leading-relaxed"> | |
| <span class="text-purple-400 font-bold block mb-1">BLOCKDIFFUSE GENERATION (100 tokens parallel block):</span> | |
| 1. Books remaining after Tuesday: 140 - 45 = 95 books.<br> | |
| 2. New total after receiving inventory on Wednesday: 95 + 80 = 175 books.<br> | |
| Answer: The store currently has 175 books remaining. <|im_end|> | |
| </div> | |
| </div> | |
| </div> | |
| </section> | |
| <!-- ========================================== --> | |
| <!-- 5. MATHEMATICAL FORMULATION WITH MATHJAX --> | |
| <!-- ========================================== --> | |
| <section id="math" class="space-y-6"> | |
| <div class="flex items-center space-x-3 text-cyan-400 font-mono text-xs uppercase tracking-widest"> | |
| <span>// Mathematical Foundations</span> | |
| <span class="h-px w-8 bg-cyan-400/40"></span> | |
| <span>Rectified Flow Matching</span> | |
| </div> | |
| <h2 class="text-3xl font-bold text-white tracking-tight">Theory & Loss Formulation</h2> | |
| <div class="glass-card p-6 rounded-2xl border border-slate-800 font-mono text-xs text-slate-200 overflow-x-auto text-center space-y-4"> | |
| <div class="text-sm text-cyan-300 font-bold"> | |
| \[ \mathcal{L}_{\text{total}} = \lambda_{\text{FM}} \mathcal{L}_{\text{FM}} + \lambda_{\text{disp}} \mathcal{L}_{\text{disp}} + \lambda_{\text{KL}} \mathcal{L}_{\text{KL}} + \lambda_{\text{CE}} \mathcal{L}_{\text{CE}} + \lambda_{\text{NN}} \mathcal{L}_{\text{NN}} \] | |
| </div> | |
| </div> | |
| <div class="grid grid-cols-1 sm:grid-cols-2 gap-4 text-xs font-mono"> | |
| <div class="p-4 rounded-xl bg-slate-900/80 border border-slate-800 space-y-1"> | |
| <span class="text-cyan-400 font-bold">1. Velocity MSE Loss (\( \mathcal{L}_{\text{FM}} \))</span> | |
| <p class="text-slate-400 leading-relaxed"> | |
| \[ \mathbb{E}_{t, z_0, z_1} \left[ \| v_\theta(z_t, t, c) - (z_1 - z_0) \|_2^2 \right] \] | |
| Matches the straight-line directional vector field towards ground-truth target latents. | |
| </p> | |
| </div> | |
| <div class="p-4 rounded-xl bg-slate-900/80 border border-slate-800 space-y-1"> | |
| <span class="text-purple-400 font-bold">2. Dispersive Repulsion Loss (\( \mathcal{L}_{\text{disp}} \))</span> | |
| <p class="text-slate-400 leading-relaxed"> | |
| \[ \frac{1}{B \cdot (K-1)} \sum_{k=1}^{K-1} \max\left(0, \cos(\hat{z}_1^k, \hat{z}_1^{k+1}) - \gamma\right) \] | |
| Forces token latents apart to eliminate degenerate identical subword repetitions. | |
| </p> | |
| </div> | |
| <div class="p-4 rounded-xl bg-slate-900/80 border border-slate-800 space-y-1"> | |
| <span class="text-pink-400 font-bold">3. Teacher KL Distillation (\( \mathcal{L}_{\text{KL}} \))</span> | |
| <p class="text-slate-400 leading-relaxed"> | |
| \[ D_{\text{KL}}\left( \text{Softmax}\left(\frac{\mathbf{W}_{\text{head}} z_1}{T}\right) \,\Big\|\, \text{Softmax}\left(\frac{\mathbf{W}_{\text{head}} \hat{z}_1}{T}\right) \right) \] | |
| Distills probability distributions across the full 151,936 vocabulary from the frozen teacher. | |
| </p> | |
| </div> | |
| <div class="p-4 rounded-xl bg-slate-900/80 border border-slate-800 space-y-1"> | |
| <span class="text-emerald-400 font-bold">4. Token Cross-Entropy & NN InfoNCE (\( \mathcal{L}_{\text{CE}}, \mathcal{L}_{\text{NN}} \))</span> | |
| <p class="text-slate-400 leading-relaxed"> | |
| Chunked discrete Cross-Entropy with gradient checkpointing + InfoNCE nearest-neighbor cosine metric learning. | |
| </p> | |
| </div> | |
| </div> | |
| </section> | |
| <!-- ========================================== --> | |
| <!-- 6. CODE QUICKSTART & CITATION --> | |
| <!-- ========================================== --> | |
| <section id="quickstart" class="space-y-6"> | |
| <div class="flex items-center space-x-3 text-cyan-400 font-mono text-xs uppercase tracking-widest"> | |
| <span>// Implementation</span> | |
| <span class="h-px w-8 bg-cyan-400/40"></span> | |
| <span>Get Started in 60 Seconds</span> | |
| </div> | |
| <h2 class="text-3xl font-bold text-white tracking-tight">Run BlockDiffuse Inference</h2> | |
| <div class="code-gradient rounded-2xl border border-slate-800 overflow-hidden text-xs font-mono shadow-2xl"> | |
| <div class="flex items-center justify-between px-4 py-2.5 bg-slate-900/90 border-b border-slate-800 text-slate-400"> | |
| <div class="flex space-x-1.5"> | |
| <div class="w-3 h-3 rounded-full bg-red-500/80"></div> | |
| <div class="w-3 h-3 rounded-full bg-yellow-500/80"></div> | |
| <div class="w-3 h-3 rounded-full bg-emerald-500/80"></div> | |
| </div> | |
| <span>bash</span> | |
| </div> | |
| <pre class="p-5 text-slate-200 overflow-x-auto leading-relaxed"><code><span class="text-slate-500"># 1. Clone repository</span> | |
| git clone https://github.com/Hooshaai/BlockDiffuse.git | |
| <span class="text-cyan-400">cd</span> BlockDiffuse | |
| <span class="text-slate-500"># 2. Install dependencies</span> | |
| pip install -r requirements.txt | |
| <span class="text-slate-500"># 3. Run parallel multi-block reasoning</span> | |
| python inference.py \ | |
| --model Qwen/Qwen2.5-0.5B-Instruct \ | |
| --checkpoint ./checkpoints_improved/blockdiffuse_final.pt \ | |
| --prompt "<span class="text-emerald-300"><|im_start|>system\nYou are a helpful assistant that solves problems step by step.<|im_end|>\n<|im_start|>user\nA bookstore has 140 books on Monday. On Tuesday, they sell 45 books. On Wednesday, they receive 80 books. How many remain?<|im_end|>\n<|im_start|>assistant\n</span>" \ | |
| --max_blocks 2 \ | |
| --steps 8 \ | |
| --solver dpm_solver \ | |
| --use_tfe \ | |
| --tfe_seeds 3</code></pre> | |
| </div> | |
| </section> | |
| <!-- 7. BibTeX Citation --> | |
| <section class="space-y-4 pt-4 border-t border-slate-800"> | |
| <h3 class="text-xl font-bold text-white">BibTeX Citation</h3> | |
| <div class="code-gradient p-4 rounded-xl border border-slate-800 font-mono text-xs text-slate-300 overflow-x-auto"> | |
| <pre><code>@article{blockdiffuse2026, | |
| title={BlockDiffuse: Fully Parallel Latent Space Reasoning Generation with Diffusion Transformers}, | |
| author={Hooshaai Research}, | |
| journal={GitHub / HuggingFace Technical Report}, | |
| year={2026}, | |
| url={https://github.com/Hooshaai/BlockDiffuse} | |
| }</code></pre> | |
| </div> | |
| </section> | |
| </main> | |
| <!-- Footer --> | |
| <footer class="border-t border-slate-800/80 bg-[#04060c] py-12 text-slate-500 text-xs font-mono"> | |
| <div class="max-w-7xl mx-auto px-4 sm:px-6 lg:px-8 flex flex-col md:flex-row items-center justify-between gap-4"> | |
| <div class="flex items-center space-x-2"> | |
| <span class="font-bold text-slate-300">BlockDiffuse</span> | |
| <span>© 2026 Hooshaai Research. Licensed under Apache 2.0.</span> | |
| </div> | |
| <div class="flex space-x-6 text-xs"> | |
| <a href="https://github.com/Hooshaai/BlockDiffuse" class="hover:text-cyan-400 transition">GitHub</a> | |
| <a href="https://huggingface.co/Hooshaai/BlockDiffuse" class="hover:text-cyan-400 transition">Model Hub</a> | |
| <a href="https://huggingface.co/datasets/Hooshaai/BlockDiffuse-Data" class="hover:text-cyan-400 transition">Dataset Hub</a> | |
| <a href="https://huggingface.co/spaces/Hooshaai/BlockDiffuse-Blog" class="hover:text-cyan-400 transition">HF Space</a> | |
| </div> | |
| </div> | |
| </footer> | |
| <!-- Interactive Simulator & Slides Script --> | |
| <script> | |
| // Slide Deck Controller | |
| let currentSlide = 0; | |
| const totalSlides = 5; | |
| function goToSlide(index) { | |
| document.querySelectorAll('.slide-content').forEach((el, idx) => { | |
| if (idx === index) { | |
| el.classList.remove('hidden'); | |
| } else { | |
| el.classList.add('hidden'); | |
| } | |
| }); | |
| document.querySelectorAll('.slide-indicator').forEach((btn, idx) => { | |
| if (idx === index) { | |
| btn.classList.add('active', 'bg-cyan-400', 'w-8'); | |
| btn.classList.remove('w-4', 'bg-slate-700'); | |
| } else { | |
| btn.classList.remove('active', 'bg-cyan-400', 'w-8'); | |
| btn.classList.add('w-4', 'bg-slate-700'); | |
| } | |
| }); | |
| currentSlide = index; | |
| document.getElementById('slide-number').textContent = index + 1; | |
| } | |
| function nextSlide() { | |
| goToSlide((currentSlide + 1) % totalSlides); | |
| } | |
| function prevSlide() { | |
| goToSlide((currentSlide - 1 + totalSlides) % totalSlides); | |
| } | |
| // ODE Trajectory Simulator | |
| const simStates = [ | |
| { | |
| step: "t = 0.0 (Pure Gaussian Noise)", | |
| flipRate: "98.4%", | |
| norm: "29.93", | |
| purity: "1.2%", | |
| text: "[Noise State] %&_@9^$# /?a9!_zx0 #82-==+ \n# All 100 positions contain unstructured Gaussian coordinates in R^896.\n# No grammatical boundaries established." | |
| }, | |
| { | |
| step: "t = 0.125 (Initial Coherence)", | |
| flipRate: "81.2%", | |
| norm: "27.42", | |
| purity: "9.5%", | |
| text: "The . . a . to . . was . is . . \n# Global syntactic cadence begins coalescing via DiT cross-attention.\n# Frequent structural anchor particles identified." | |
| }, | |
| { | |
| step: "t = 0.25 (Sentence Boundaries)", | |
| flipRate: "64.7%", | |
| norm: "24.15", | |
| purity: "24.8%", | |
| text: "Step 1 : First , the total books on Monday ... \n# Sentence structure and numbered list token positions begin stabilizing.\n# Numerical operation candidates form in continuous space." | |
| }, | |
| { | |
| step: "t = 0.375 (Semantic Anchoring)", | |
| flipRate: "49.1%", | |
| norm: "21.60", | |
| purity: "42.0%", | |
| text: "Step 1: Start with 140 books . On Tuesday they sold 45 books . \n# Mathematical facts extracted from prompt prefix.\n# Subtraction intent strongly aligned across target latents." | |
| }, | |
| { | |
| step: "t = 0.5 (Midpoint Trajectory)", | |
| flipRate: "33.5%", | |
| norm: "18.84", | |
| purity: "63.7%", | |
| text: "Step 1: Calculate remaining after Tuesday: 140 - 45 = 95 books . \n# Calculation result (95) locks in across continuous representations.\n# Dispersive loss eliminates redundant subwords." | |
| }, | |
| { | |
| step: "t = 0.625 (Second-Order Refinement)", | |
| flipRate: "19.8%", | |
| norm: "16.12", | |
| purity: "81.4%", | |
| text: "Step 2: On Wednesday, they received 80 new books. \nSo we compute 95 + 80 = 175 books remaining . \n# Addition operation successfully grounded." | |
| }, | |
| { | |
| step: "t = 0.75 (Formatting & Conclusion)", | |
| flipRate: "10.2%", | |
| norm: "14.28", | |
| purity: "92.6%", | |
| text: "Step 1: 140 - 45 = 95 books remaining.\nStep 2: 95 + 80 = 175 books total.\nTherefore, 175 books remain in the store." | |
| }, | |
| { | |
| step: "t = 0.875 (Punctuation Fine-Tuning)", | |
| flipRate: "4.1%", | |
| norm: "13.04", | |
| purity: "97.9%", | |
| text: "1. After selling 45 books: 140 - 45 = 95 books.\n2. After receiving 80 books: 95 + 80 = 175 books.\nFinal Answer: The store has 175 books remaining." | |
| }, | |
| { | |
| step: "t = 1.0 (Clean Discrete Output)", | |
| flipRate: "0.8%", | |
| norm: "12.45", | |
| purity: "99.9%", | |
| text: "1. Monday initial count: 140 books.\n2. Tuesday after selling 45: 140 - 45 = 95 books.\n3. Wednesday after receiving 80: 95 + 80 = 175 books.\nFinal Answer: There are 175 books remaining. <|im_end|>" | |
| } | |
| ]; | |
| function updateODESimulation(val) { | |
| const state = simStates[val]; | |
| document.getElementById('step-label').textContent = state.step; | |
| document.getElementById('step-count').textContent = `Step ${val} / 8`; | |
| document.getElementById('sim-flip-rate').textContent = state.flipRate; | |
| document.getElementById('sim-norm').textContent = state.norm; | |
| document.getElementById('sim-purity').textContent = state.purity; | |
| document.getElementById('sim-decoded-text').textContent = state.text; | |
| } | |
| let isPlaying = false; | |
| function playSimulation() { | |
| if (isPlaying) return; | |
| isPlaying = true; | |
| let current = 0; | |
| const slider = document.getElementById('ode-slider'); | |
| const interval = setInterval(() => { | |
| slider.value = current; | |
| updateODESimulation(current); | |
| current++; | |
| if (current > 8) { | |
| clearInterval(interval); | |
| isPlaying = false; | |
| } | |
| }, 550); | |
| } | |
| </script> | |
| </body> | |
| </html> | |